We invite contributions that examine functional diversity in motion: from shifts in community composition and trait distributions to adaptation via evolutionary change. We particularly welcome studies that link trait dynamics to biogeochemical consequences, whether through experiments, observational time series, comparative biogeography, or trait-based and eco-evolutionary models. Contributions may address open questions such as: How do ecological and evolutionary processes interact to drive functional change? Can trait distributions predict ecosystem responses to perturbations? How transferable are eco-evolutionary insights across biomes and scales?
By bringing together work across soils, vegetation, freshwater, and marine systems, this session aims to foster a cross-system perspective on the dynamic links between diversity, adaptation, and biogeochemical function.
Orals: Mon, 4 May, 08:30–12:30 | Room 1.85/86
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.
How to cite: Schwarz, E., Abs, E., Chakrawal, A., Chavez Rodriguez, L., Quévreux, P., Reitz, T., and Manzoni, S.: Eco-evolutionary optimization in soil organic matter models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3995, https://doi.org/10.5194/egusphere-egu26-3995, 2026.
Syntrophy is metabolic cross-feeding in which an upstream organism can oxidize a substrate only because a partner continuously removes inhibitory products (often H2), making the overall reaction energetically favorable. In soils, moisture regulates anaerobic microbial interactions by shaping oxygen availability and gas diffusivity, while fermentation produces reduced intermediates, including volatile fatty acids (VFAs) such as butyrate and propionate, whose oxidation is endergonic under standard conditions and becomes feasible only when hydrogen is maintained sufficiently low by hydrogenotrophic methanogens. Here we present a minimalist predator–prey model that captures the key feedbacks among moisture, hydrogen dynamics, and methanogen biomass. Moisture modulate hydrogen production, leakage, and methanogenic growth, shifting the system between a hydrogen-accumulating, methanogen-free regime and a syntrophic coexistence regime in which methanogens depress hydrogen below the threshold required for VFA oxidation to become exergonic. The resulting moisture-driven transition is a transcritical bifurcation governed by a moisture-dependent methanogen reproduction number, providing a compact link between hydrologic variability and the onset and collapse of syntrophy in soils.
How to cite: Porporato, A., Calabrese, S., and Olaitan, D.: EcoHydrology, Thermodynamics, and Microbial Ecology at the onset of soil syntrophy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21544, https://doi.org/10.5194/egusphere-egu26-21544, 2026.
Earth system models (ESMs) represent the pinnacle of our ability to understand and predict Earth system dynamics, and are constructed from submodels that should capture the processes and process interactions occurring in the atmosphere, oceans, on land. One microbial parameter that defines key feedbacks in the Earth system is the microbial temperature dependence, e.g. for decomposition. Submodules within past (e.g. CENTURY, Roth-C) and future (Millennial, MEMS) ESMs represent this with one intrinsic temperature dependence for decomposition, and extending this static temperature dependence (i.e., unchanging) to all microbial processes (organic matter formation or destruction, etc.) and assuming no differences among climates across the globe.
Global microbial diversity has been mapped with -omics, revealing incredibly diverse, versatile and biogeochemically active microbes. However, the central challenge stubbornly persists – translating microbial diversity into quantitative representations that capture ecosystem processes. This inability forms a barrier for integration of microbial ecology into ESMs.
We use instantaneous measurements of microbial processes to estimate microbial intrinsic temperature dependences as “trait distributions” in situ, in environmental samples. We can thus translate biodiversity into ecosystem functions, and generate mathematical descriptions that interface with ESMs. We have uncovered how intrinsic microbial temperature dependences for processes that form (growth) and destroy (decomposition) organic matter vary across the globe, across seasons, and respond to warming. We have unearthed how temperature trait distributions interact with those for moisture, and determined the ecological and evolutionary mechanisms underpinning change. Our insights can be integrated into existing ESMs, revealing that dynamic microbial feedbacks characterise the earth system.
How to cite: Rousk, J.: Solving the microbial temperature problem in Earth system science, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6873, https://doi.org/10.5194/egusphere-egu26-6873, 2026.
Wildfires are ubiquitous and an integral part of the Earth System, vital for maintaining the biodiversity and functioning of many ecosystems. Wildfire-induced changes in vegetation and landscape properties also have important feedbacks to climate through modulating water- and energy-exchanges and the carbon cycle. The current state-of-the-art global models used to predict how wildfires might behave in a changing climate capture some aspects of wildfire behaviour, but are poor at simulating fire seasonality, interannual variability and extreme fires, in large part because they do not adequately capture the vegetation-wildfire interactions regulating fire occurrence. Eco-evolutionary optimality approaches are increasingly being used to provide simple but robust models of vegetation functioning, and here we extend this approach to modelling wildfires.
Fuel availability and fuel dryness are consistently shown to be the primary drivers of wildfire occurrence, intensity and burnt area. Differences in the timing of fuel build up and drying determine the optimal time for wildfire occurrence and give rise to pyroclimates with distinct wildfire regimes. The phase difference in the seasonal time course and magnitude of gross primary production (GPP) and vapour pressure deficit (VPD) is used to provide a measure of the “propensity to burn”, which in turn can be translated into a probability for fire occurrence. An EEO-based model of the seasonal cycle of GPP is then used to derive litter fall and hence the inputs to dead fuel loads along with an empirically based formulation of decomposition to determine changes in the actual dead fuel load through time. We use an EEO-based model of biomass production efficiency to derive tree and grass cover, where the grass cover and dead fuel load together will determine the incidence of ground fires and tree cover the incidence of crown fires. We show that this simple model produces realistic simulations of spatial and temporal patterns in wildfire occurrence, and thus provides a basis for simulating the impact of wildfires on vegetation loss, post-fire recovery and ultimately feedbacks to climate.
How to cite: Harrison, S. P., Cain, S., Ding, R., Sandoval Calle, D., Zhou, B., and Prentice, I. C.: An eco-evolutionary approach to modelling wildfire regimes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5781, https://doi.org/10.5194/egusphere-egu26-5781, 2026.
Plankton communities are an essential component of ocean biogeochemistry and play a key role in making oceans an important climatic buffer. In the oceans, the environmental control of planktonic activity is modulated by the composition and diversity of plankton physiological traits (e.g., size, temperature and light preferences, stoichiometry, etc.). Yet, very little is known about how plankton communities assemble in the ocean under the combined influence of biological (eco-evolutionary dynamics) and physical mechanisms (mixing, transport). Moreover, this key process is very crudely represented for in current ocean models. Here, I show how integrating Darwinian adaptation into ocean models allows simulating how the functional composition and diversity of plankton communities is shaped by adaptation and ocean physics, how it feeds back on ocean biogeochemistry, and what the implications are for the resilience of marine ecosystems under climate change.
How to cite: Sauterey, B., Torres, O., Aumont, O., Le Gland, G., Cermeño, P., Vallina, S., and Bopp, L.: Darwinian adaptation of plankton in global ocean models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5586, https://doi.org/10.5194/egusphere-egu26-5586, 2026.
Most ecosystems are characterized by a rich and dynamic landscape of functional diversity. Ecological interactions that drive biodiversity and adaptation are profoundly complex — they arise from fine‐scale variation in organismal traits, unfold across ecological and evolutionary timescales, and operate within dynamic ever-changing environments. An individual’s performance, and thus its contribution to community structure and ecosystem functioning, emerges from the following key factors: (1) its physiological state (such as size, age, or energy reserves), (2) its capacity to acclimate to short-term microclimatic changes, (3) trait-mediated trade-offs it faces in growth and survival, (4) the traits and states of other organisms in the community, and (5) the long-term abiotic environment which itself may be co-created by the population. To understand how functional diversity is filtered and reshaped by these processes, we need a theory that can play out long-term eco-evolutionary dynamics of ecosystems while incorporating realistic ecological complexity.
Here, we introduce a unified trait-based eco-evolutionary framework that meets this challenge by explicitly integrating three core features of real ecosystems: (1) continuous physiological state structure, (2) intraspecific and interspecific trait variation, and (3) frequency-dependent selection driven by population–environment feedbacks. The framework can be coupled to trait-based eco-physiological models of individual performance, allowing short-term acclimation and long-term evolution to be treated within a single, coherent system. This makes it possible to predict the best-adapted trait combinations under different environments, to test whether physiological trade-offs encoded in models are consistent with observed trait distributions along environmental gradients, and to project how those distributions will shift under future short- and long-term environmental change. At the same time, the approach provides a scalable alternative to computationally intensive individual-based models while retaining key sources of ecological and evolutionary complexity.
We apply this framework to predict plant hydraulic strategies across environmental gradients by coupling it with the Plant-FATE model, which accounts for physiological acclimation of individuals and trait-size-structured vegetation demographics of populations. The theory predicts that, all else being equal, plants evolve more negative xylem vulnerability (P50) in drier environments, matching broad empirical patterns across real ecosystems. This agreement provides an evolutionarily grounded validation of the functional trade-offs embedded in plant physiology and enables robust forecasts of how trait distributions — and their biogeochemical implications — are likely to respond to ongoing environmental change.
How to cite: Joshi, J., Vignal, T., and Dieckmann, U.: Functional diversity in motion: a general theory of eco-evolutionary change in complex ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12325, https://doi.org/10.5194/egusphere-egu26-12325, 2026.
Carbon (C) allocation refers to the processes by which plants distribute assimilated C among growth, storage, and respiration. While most ecosystem and land surface models explicitly represent C allocation, its treatment in many models remains rudimentary, reflecting a lack of consensus and limiting their ability to capture the processes governing C partitioning. A long-standing theory explains C allocation as maximizing growth, with foliage and below-ground investments balancing light, water and nutrients availability. However, the large C investment in tree stems does not contribute to primary production but reflects an evolutionary strategy to maximize light capture and competitive ability. Biomass production efficiency (BPE) quantifies the efficiency of assimilated C that is converted into structural growth. It reflects the balance between C gain by photosynthesis and C losses, principally autotrophic respiration (Ra). However, the controls on BPE remain poorly constrained, and even the sign of its response to growth temperature is unclear. Here we develop robust semi-empirical models of C allocation of forest dynamics, maximum tree height (Hm) and BPE in order to explore how C partitioning is influenced by the availability of different resources. We hypothesize that the demands of foliage production, and concomitant below-ground production to support that foliage, are satisfied with highest priority; and that any excess C (the net C profit, Pn) is allocated to stems in such a way as to maximize height growth, as a strategy for competitive fitness. Under this framework, the average diameter growth of a tree, and Hm, in an even-aged forest are shown to be proportional to Pn. We further show that BPE is shown to decrease with growth temperature (Tg), stand age, soil C:N ratio, pH and sand content, while increasing with mean temperature of the coldest month—resolving a contradiction in the literature, about its apparent response to mean annual temperature—and to be greater for deciduous than evergreen woody plants. These findings contribute to an optimality-based theoretical framework for improved process-based C allocation modelling in forest ecosystem models.
How to cite: Ding, R., Harrison, S., and Prentice, I. C.: Predicting forest dynamics and biomass production efficiency based on optimality principles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5626, https://doi.org/10.5194/egusphere-egu26-5626, 2026.
Diurnal patterns of photosynthesis of ecosystems are theoretically expected to mimic those of incoming solar radiation (SW) and peaks at noon. By examining global ecosystem eddy covariance observations, however, we found ecosystem photosynthesis often peaks before noon, indicating widespread midday or afternoon photosynthesis depression. While some studies have attributed this depression to stomatal closure, a strategy that limits water loss under high atmospheric vapor pressure deficit (VPD), leaf-level studies suggest that excess light can trigger photoprotective responses and also cause the depression. Following the hypothesis, we studied the gaps between ecosystem carbon uptake peak and that of SW (0.48 ± 0.26 h), and found that the gaps advance increases with SW even on site-days characterized by the lowest VPD. Biomes receiving the highest SW, such as savannas and evergreen broadleaf forests, exhibit the largest gap between carbon uptake peak and SW peak. Together, these findings indicate that excess light is a key yet underappreciated driver of ecosystem-scale midday depression. Incorporating light-driven photoprotective processes into terrestrial carbon models may improve simulations of diurnal carbon fluxes.
How to cite: Yu, L. and Luo, X.: Widespread pre-noon photosynthesis peak driven by afternoon photoprotection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4785, https://doi.org/10.5194/egusphere-egu26-4785, 2026.
The terrestrial biosphere constitutes a major component of the global carbon cycle, absorbing a substantial fraction of anthropogenic CO2 emissions and thereby mitigating climate change. Terrestrial vegetation governs the largest carbon flux in biosphere - gross primary production (GPP), the total carbon uptake through photosynthesis - making accurate quantification of GPP critical to projection of land-atmosphere carbon exchange. However, it remains challenging due to uncertainties in observations and model representations. Advances in high-resolution satellite remote sensing products now enable detailed monitoring of vegetation changes, while process-based models could offer mechanistically robust characterization of plant biophysical and biochemical processes. Here we integrate quality-controlled and corrected Sentinel-2 leaf area index (LAI) with eco-evolutionary optimality-based P model to simulate GPP at eddy covariance flux sites. Model performance is evaluated against site observations to assess the ability of this framework to reproduce observed spatial patterns and temporal dynamics. Our results demonstrate that such hybrid approaches combining Earth Observation data with a theoretically grounded, parameter-sparse model greatly improved GPP simulation, highlighting a promising pathway for advancing ecosystem carbon flux modelling and evaluation.
How to cite: Cai, W., Prentice, I. C., Hong, H., Yu, W., and Ryu, Y.: Improving Gross Primary Production Estimates by Integrating Eco-Evolutionary Optimality Modelling with High-Resolution Sentinel-2 Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14477, https://doi.org/10.5194/egusphere-egu26-14477, 2026.
Viruses are the most abundant biological entities on Earth and exert powerful controls on ecosystem ecology, biogeochemical cycling, and microbial evolution. Hypersaline environments host the highest reported viral abundances of any aquatic system, yet little is known about how salinity and other environmental extremes influence viral ecology. In these systems, salinity alongside acidity strongly influence the isoelectric point (pI) of viral particles – the pH at which a virion carries no net surface charge – which affects viral particle electrostatic interactions and stability. When environmental pH approaches the pI of the viral structural proteome, virions lose surface charge, aggregate, and adsorb to particles, reducing viral infectivity. This, in turn, greatly influences microbial ecology and ecosystem-scale biogeochemical cycling. In this study, we use acidic and alkaline hypersaline lakes in Western Australia as a natural Earth-system laboratory to test how pH and salinity shape viral ecogenomics and microbial evolution. We analyzed metagenomes and viromes from 37 polyextreme lakes spanning pH 2.3-9.4 and 30-465 ppt salinity across wet and dry seasons, recovering 11,804 viral populations from 50 families along with 645 microbial metagenome-assembled genomes. We calculated the pI for viral structural proteomes and placed these data in a global context using viral genomes from environments spanning freshwater, soda lakes, acidic meromictic lakes, and deep-sea hydrothermal vents. Across all environments, viral structural pI distributions were strongly skewed toward more acidic values, with the most acidic capsids occurring in hypersaline and alkaline brines. Even modest shifts in viral structural pIs (~0.8 pH units) correspond to order-of-magnitude changes in proton concentration, suggesting physicochemical selection. Within cosmopolitan viral families, structural pI shifted systematically across pH-salinity regimes, demonstrating that structural traits are not fixed by phylogeny alone but respond to environmental geochemistry. Viruses infecting halophilic archaea exhibited the most acidic and most tightly constrained structural pI values, pointing to host envelope chemistry and host ecology as an additional filter on viral evolution. To understand how viruses may influence microbial adaptation to these environmental extremes, we also functionally characterized viral auxiliary metabolic genes (AMGs) and genes encoded on plasmids. Viral AMGs primarily supported host energy metabolism rather than stress tolerance, whereas plasmids encoded extensive osmotic and acid-stress pathways that were strongly structured across pH-salinity space, identifying plasmids as key agents of microbial adaptation in extreme brines. By linking viral and plasmid omics to geochemical gradients across a natural Earth-system laboratory, this work shows how molecular-scale traits scale up to shape ecosystem function and biogeochemical dynamics across the planet.
How to cite: Skoog, E. J., Klempay, B., Weng, M. M., Fisher, L. A., Plattner, T., Schmidt, B. E., and Bowman, J. S. and the OAST Team: Acidity and salinity influence viral ecogenomics and microbial evolution in polyextreme lakes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19252, https://doi.org/10.5194/egusphere-egu26-19252, 2026.
The Arctic is experiencing rapid climate change, with warming rates exceeding three to four times the global average. This has a profound impact on cloud and precipitation formation. Bioaerosols are critical for cloud processes as they can act as high-temperature ice nucleating particles (INPs). Despite their importance, the understanding of bioaerosol-cloud interactions remains highly uncertain, primarily due to limited information on the types, concentrations, and sources of biogenic INPs. To reduce these uncertainties, we combined analyses of Arctic soils as potential reservoirs of biogenic INPs with multi-year atmospheric observations of bioaerosols and INP in the High Arctic.
We first investigated Arctic soils as reservoirs of biogenic INPs by analyzing fungal community composition and INP concentrations across 78 soil samples collected from seven sites spanning southern to northern Greenland. To determine whether INPs from soils are transferred into the atmosphere, we performed the first multi-year (2021–2023) study of bioaerosol abundance and composition, together with quantifying high-temperature INPs from the High Arctic, collected at Villum Research Station at a 3.5-day time resolution. Soils were sieved and INPs associated with particles <5 µm as well as INPs found in the soluble fraction (<0.22 µm) were obtained using the Micro-PINGUIN assay. Fungal and bacterial communities were characterized using ITS2 and 16S rRNA gene amplicon sequencing. Source tracking was used to determine the contribution of local sources to airborne microbial cells and INP.
In the soils, we found that higher INP concentrations were associated with higher latitudes. Based on their high-temperature activity, we suggest that these INPs are proteinaceous. Using multivariate analyses, we identified annual mean air temperature as the dominant explanatory variable, followed by soil pH. The composition of the fungal community varied significantly among sites, and several taxa, including Leptosphaeria, Pseudogymnoascus, Tetracladium, and Microdochium, showed significant positive correlations with high-temperature INP concentrations, suggesting that members of the fungal community are producing soil-derived INPs. We found that the INPs were present in the soluble fraction of the soils, which is also consistent with fungal origin. As suggested for temperate regions, these INPs can disassociate from fungal hyphae and bind to clay particles, getting emitted to the atmosphere on inorganic particles. Analyzing aerosol samples, we found that atmospheric INP concentrations ranged from 2.2 × 10-2 to 7.2 × 101 m-3, and airborne bacterial concentrations from 2.7 × 100 to 4.2 × 103 m⁻3. We observed seasonal shifts in microbial community composition, with spore-forming taxa dominating during in spring and more diverse, locally sourced communities in summer. Both bacterial abundance and diversity were positively correlated with warm-temperature INP concentrations, indicating that these were associated with emissions from environments with dense and diverse bacterial communities, such as soils.
Together, our results allow us to link high-latitude terrestrial microbial communities to atmospheric INP, and we demonstrated that Arctic soils, particularly at northern latitudes, represent key reservoirs of biogenic INPs, which disperse into the atmosphere. By integrating studies of the microbial soil communities and long-term atmospheric observations we can constraint biological aerosol–cloud interactions and their potential sensitivity to the ongoing Arctic warming.
How to cite: Šantl-Temkiv, T., Jensen, L. Z., Lamesta, T., Castenschiold, C. D. F., Kumari, S. P., Massling, A., Skov, H., Stratmann, F., Wex, H., and Finster, K.: From Arctic soils to the atmosphere: microbial controls on biological ice-nucleating particles at high latitudes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16163, https://doi.org/10.5194/egusphere-egu26-16163, 2026.
Fungi are highly efficient degraders of organic matter, including recalcitrant compounds, and are therefore key recyclers in global biogeochemical cycles. While the vast majority of fungal diversity has been studied in terrestrial environments, marine fungi remain largely underexplored despite their ecological relevance and growing biotechnological interest. Notably, only ~1% of described fungal species originate from marine environments, and many of these are also found on land, raising the question of whether environmental adaptability is driven by species-level traits or by strain-level plasticity.
To address this, we compared worldwide strains of the same fungal species isolated from terrestrial and marine environments, integrating genomic analyses with detailed phenotypic assays. Our study focused primarily on the genus Trichoderma, a taxa with key roles in decomposition, plant-fungus interactions, and industrial enzyme production, including the cellulase-producing workhorse Trichoderma reesei, which served as key reference system.
While genome content was largely conserved across strains, pronounced phenotypic divergence was observed between marine and terrestrial isolates regarding salinity tolerance, and divergent metabolic niches through distinct carbon source preferences and altered rhizosphere interactions, even under saline conditions. These results suggest that environmental adaptation in Trichoderma is primarily driven by physiological plasticity rather than major genomic restructuring, indicating a broad physiological reaction norm that allows for the colonization of diverse saline and non-saline habitats.
Our findings highlight marine fungi as overlooked reservoirs of adaptive traits relevant to biogeochemical processes and biotechnology, including enzyme production, metabolite diversity, and stress-resilient plant–fungus interactions. By linking ecological origin to phenotypic performance, this study underscores the evolutionary plasticity of marine fungi and their potential role in shaping resilient bioprocesses and ecosystem functioning in a changing planet.
How to cite: Benocci, T., Zaragoza, A., Anthony, M., Baltar, F., and Baroncelli, R.: Fungi from land to sea: phenotypic plasticity drives functional adaptation across saline and non-saline habitats, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13459, https://doi.org/10.5194/egusphere-egu26-13459, 2026.
Understanding and predicting the feedback between climate change and soil carbon dynamics remains a major scientific challenge. A key uncertainty lies in our limited knowledge of how changing hydrothermal conditions influence microbial functional dynamics and their contributions to soil carbon emissions. In particular, the microbial functions that respond to hydrothermal variability—and their interactions with functions involved in soil carbon and nutrient cycling—remain poorly characterized. It is still unclear how both historical and current hydrothermal conditions affect the relative abundances of these microbial functions and how these shifts impact the dynamics of soil carbon emission in response to changing hydroclimate. To fill these knowledge gaps, we combined gene-to-ecosystem data from key ecological networks to develop artificial intelligence models to identify and quantify microbial resource allocation strategies in response to past and present hydrothermal properties. Our findings indicated that microbial communities acclimated to reduced soil moisture by lowering investment in recalcitrant-C decomposition and monomer nutrient mineralization. This drought-mitigation response was amplified by drying legacy but dampened by nutrient limitation. Elevated soil temperature, in contrast, generally increased microbial investment in N acquisition, while thermal legacy strengthened the thermal resistance of N-acquisition allocation and promoted reallocation of C- and P-acquiring functions toward adaptation to current hydrothermal dynamics. Finally, we will show how the identified resource optimization strategies can be applied to interpret observed soil carbon dynamics under climate change and to advance earth system modeling of soil carbon emissions.
How to cite: Song, Y., Fan, C., and Wilson, S.: Past and present hydrothermal regimes shape microbial resource allocation for soil C, N, and P cycling: insights from machine-learning predictions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19305, https://doi.org/10.5194/egusphere-egu26-19305, 2026.
Marine heatwaves (MHWs) are becoming more frequent, intense, and prolonged, posing increasing threats to marine ecosystems, including phytoplankton communities. Yet, understanding the impacts of MHWs on phytoplankton community structure remains challenging, given the limited number of observational and process-resolving modeling studies. Here, we develop a modeling framework using an advanced coupled ocean–biogeochemical model (MITgcm–Darwin), in which biogeochemical processes for 310 types of phytoplankton are explicitly resolved. In this model, 310 types are defined by different combinations of key traits: 14 size classes, 10 temperature preferences, and 8 ecological functions. We find an overall shift in phytoplankton composition toward small and warm-preferring types during MHWs. However, detailed features differ substantially across regions and traits. For example, in the tropical Pacific Ocean, the magnitude of shifts tends to increase with heatwave intensity, for both size and temperature traits. A moderate influence of the duration on the temperature trait is also found. In the Indian Ocean, on the other hand, heatwave intensity is the primary factor that affects size composition, while no significant shifts in temperature preference are detected. For both regions, these composition shifts are accompanied by significant losses in biodiversity, reflected in decreased richness and evenness. These results indicate that even short-term climatic extremes can substantially disrupt phytoplankton communities, with potential increasing consequences for marine food webs and ecosystem functioning that depend on phytoplankton as such perturbations intensify.
How to cite: Kim, H., Song, H., Dutkiewicz, S., Lee, J., Hoteit, I., and Wang, Y.: Heat Stress-Driven Shifts in Marine Phytoplankton Trait Composition in a Global Ocean-Biogeochemical Model , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6247, https://doi.org/10.5194/egusphere-egu26-6247, 2026.
Although the macromolecular composition of phytoplankton shapes the nutrition available to marine ecosystems and regulates global biogeochemistry, there are no mechanistic, predictive models for its global distribution. Using a cellular allocation model, we simulate phytoplankton allocation to proteins, carbohydrates, and lipids in the present day and a warming scenario. Our simulations predict spatial variations consistent with available observations: in nutrient-sufficient, low-light high-latitude regions, phytoplankton allocate more to nitrogen-rich proteins, while in nutrient-depleted subtropical regions, allocation favours carbohydrates and lipids. Under warming, subtropical phytoplankton increase protein allocation by ~20%, as subsurface populations, rich in light-harvesting protein, thrive, whereas high latitude protein allocation declines by ~15–30% due to warming and light limitation relief. In situ macromolecular measurements in polar regions show recent trends consistent with our predictions. These results suggest that macromolecular composition responds measurably to changing environmental conditions, reshaping the nutritional landscape at the base of the marine food web.
How to cite: sharoni, S., Inomura, K., Dutkiewicz, S., Jahn, O., Finkel, Z., Irwin, A., Amirian, M. M., Monier, E., and Follows, M.: Biochemical remodeling of phytoplankton cell composition under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14116, https://doi.org/10.5194/egusphere-egu26-14116, 2026.
Reliable simulation of carbon and water fluxes in forest ecosystems is essential for understanding global energy, carbon, and water cycles, while it remains limited by the large number of poorly constrained parameters in land surface models, particularly in regions lacking flux observations. While model-data integration using satellite and eddy covariance data has improved performance, it does not resolve the fundamental problem of parameter identifiability.
Here, we use SINDBAD (Koirala et al., 2025), a model-data integration framework, to evaluate whether eco evolutionary optimality (EEO) principles can act as effective constraints on a coupled carbon water land surface model when flux observations are unavailable. Using 37 forest sites worldwide spanning 1979-2017, we compare three experiments that differ in the type of constraints applied, i.e., vegetation structure only, vegetation structure plus flux observations, and vegetation structure plus EEO based constraints, to assess to what extent theoretical optimality principles can help even without direct flux information.
We find that vegetation structure alone is insufficient to reproduce observed carbon and water fluxes, especially at water limited sites. Incorporating EEO constraints leads to clear improvements in simulations of gross primary productivity, ecosystem respiration, and evapotranspiration under water limitation, while effects are weaker at energy limited sites. EEO constrained simulations also show more realistic sensitivities of fluxes to precipitation and temperature, in some cases exceeding those obtained when flux observations are directly assimilated.
These results suggest that eco evolutionary optimality principles can provide meaningful constraints on land surface models with high dimensional parameter spaces, reducing effective parameter uncertainty under data sparse conditions.
How to cite: Zhou, J., Carvalhais, N., Hildebrandt, A., Koirala, S., and Jiang, S.: Simulating Forest Carbon-Water Fluxes in Land Surface Models through Eco-Evolutionary Optimality Principles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13111, https://doi.org/10.5194/egusphere-egu26-13111, 2026.
Recent theory suggests that the evolutionary adaptation of soil microbial communities to climate change could significantly aggravate the currently predicted global soil carbon loss in response to global warming through the selection of gene variants affecting carbon-cycling traits (e.g. respiration, decomposition or secondary metabolites production).
However, empirical evidence is still lacking to quantify the rate and magnitude of evolutionary changes in carbon-cycling traits across bacterial functional groups. This gap limits the integration of microbial evolutionary responses into carbon biogeochemical models.
We analysed long-term (10 years) high throughput metagenomic time series from two global change experiments: the SPRUCE peatland experiment (warming and elevated CO₂) and the Loma Ridge grassland drought experiment. We combined classical metagenomic analyses (read alignment, SNP detection) with collapsing gene-level variation into functional trait categories.
Focusing on the most abundant Metagenomes Assembled Genomes (MAGs), e.g. Acidocella sp., (> 10X and 50% of coverage), we identified genes showing signs of adaptive evolution associated with carbon-cycling traits, revealing which traits exhibit the strongest evolutionary responses under climate-change treatments such as traits involved in cellulose degradation.
These results provide a framework to link metagenomic time series with process-based carbon models by defining empirical-based evolutionary markers of climate-change response, enabling the explicit inclusion of microbial evolutionary dynamics in global carbon models such as ORCHIDEE.
How to cite: Bourreau, M., Abs, E., and Chase, A.: Evolutionary adaptation of soil microbial communities to climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3488, https://doi.org/10.5194/egusphere-egu26-3488, 2026.
Soil microorganisms play a critical role in global carbon fluxes and shape local biogeochemical cycles through their vast functional diversity, yet it remains unclear how this diversity influences soil carbon fluxes at the global scale. For example, unlike plants, which are almost uniformly autotrophic, microbial communities encompass a wide range of substrate use : however, current models lack a simplified, yet representative framework to capture this functional diversity, limiting our ability to accurately predict biogeochemical cycling in a changing climate.
To address this, we propose a trait-based microbial functional classification that leverages the growing availability of metagenomic data. Using the microTrait tool, we analyze trait information from a global database of 40,000 metagenome-assembled genomes (MAGs) to compare several clustering methods with multiple quality metrics, and define ecologically meaningful functional groups.
By backmapping MAGs to their original metagenome, we obtain relative abundance data, allowing us to examine how microbial community composition varies across environmental gradients of soil, climatic and biotic parameters. Our analysis reveals some associations between community structure and environmental parameters, suggesting that integrating microbial functional traits into soil models could improve biogeochemical predictions.
How to cite: Richard, E. and Abs, E.: Mapping global functional diversity of soil microbes using metagenomics data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3509, https://doi.org/10.5194/egusphere-egu26-3509, 2026.
Soil microbial communities control the fate of the largest terrestrial organic carbon pool, and their decomposition and respiration dynamics are pivotal for predicting future climate feedbacks. Community diversity, functional complexity, and adaptive responses may substantially reshape projections of the global carbon cycle.
Yet, most microbial-explicit soil biogeochemical models rely on simplified communities with static traits (e.g. growth and respiration). Approaches that incorporate microbial diversity and evolutionary processes remain largely theoretical and poorly constrained by empirical diversity and geochemical measurements, limiting their applicability in Earth system model predictions.
Here, we bridge this gap by fitting microbial community adaptation to warming using a genomics-informed, agent-based microbial model (DEMENT). We develop a framework to parameterize realistic microbial communities from metagenome-assembled genomes (MAGs), capturing taxon-specific traits related to enzyme production, resource uptake, and carbon allocation. Using long-term soil warming experiments at the Harvard Forest LTER site as a case study, we explicitly simulate the temporal dynamics of microbial community composition, respiration, and organic matter degradation under warming. We evaluate alternative evolutionary scenarios of microbial adaptation; targeting resource acquisition, growth yield, and stress responses; and identify the scenario that best reproduces observed diversity patterns as well as post-adaptation growth and respiration responses across temperature gradients.
This approach enables the identification of evolutionary pathways underlying microbial community responses to warming and provides a critical foundation for integrating adaptive microbial processes into next-generation Earth system models.
How to cite: Cortier, T. and Abs, E.: Fitting microbial community adaptation of respiration and growth to warming using a genomics-informed agent-based model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3520, https://doi.org/10.5194/egusphere-egu26-3520, 2026.
The exoskeleton of crabs serves functions in protection, support, and sensing. Among the microstructures that compose the exoskeleton, the Bouligand structure is known to contribute to its mechanical properties. Previous research on the influence of microstructures on the mechanical properties of the crustacean exoskeleton has primarily focused on stacking height (SH), yet it remained controversial whether SH is the dominant determining factor of the mechanical properties. In this study, we comprehensively analyzed the pitch angle, diameter of the chitin-protein fiber, and the interlamellar spacing in the Bouligand structure to compare their contribution to the mechanical properties. We found that in vent crabs, the carapace was harder than the claw, while the opposite was observed in ghost crabs. In vent crabs, SH was 1.95 times greater than in the claw, a difference likely attributable to the pitch angle-the only microstructural feature that varied. In contrast, no structural differences were detected between regions in ghost crabs, where SH was extremely small (< 1 μm) and thus mechanical properties appear to be governed by material characteristics rather than structure. These findings indicate that pitch angle influences the mechanical properties of the crab exoskeleton only when SH is sufficiently large.
How to cite: Hong, J., Cho, B., Kim, D., Jang, S.-J., Kang, M., Yoon, S., and Kim, T.: Microstructural determinants of mechanical properties in exoskeletons: a comparison between hydrothermal vent crab and ghost crab, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6181, https://doi.org/10.5194/egusphere-egu26-6181, 2026.
A large diversity of microorganisms lives in soils where they transform the available organic matter, and store or release into the atmosphere the carbon it contains. Individual cells of the same or different species interact together in metabolic networks, i.e. networks of interactions ranging from competition for a same resource to cooperation with an exchange of resources. Because soil is a very heterogeneous environment, these interactions are limited by the local presence of resources and species. Therefore, all the theoretically possible interactions are not realized in practice. Understanding the impact of spatial heterogeneity on soil metabolic networks is essential to improve our comprehension of the carbon cycle in soils. However, it remains very difficult today to study spatial heterogeneity and metabolic networks in situ.
Here, we present a numerical model we developed to study the impact of microbial spatial distributions on metabolic networks. Our model is spatially explicit and individual based. Each cell has a spatially limited impact on its environment, in which it is able to take up some resources and transform them into other products, which are then released into the environment and can be used by other cells.
In this work, we explore the emergence of a type of interaction that only arise when spatial heterogeneity is taken into account, the eclipse dilemma (a concept first developed in Metabolic Resource Allocation in Individual Microbes Determines Ecosystem Interactions and Spatial Dynamics, Harcombe et al., 2014): in some spatial configurations, two individuals competing for the same resource can eventually enter a cooperating dynamic by providing to a common partner species with which they exchange resources. We have found that while competition for the same resource reduces the average amount of resource that each individual can obtain due to sharing, cooperation with a common partner can lead to a local increase in available resources that can exceed the effect of competition. Those local increases in variability of metabolic interactions showed that spatialization in soil models is indeed essential to a proper microbial representation in models.
How to cite: Paris, J.-M., Raynaud, X., and Nunan, N.: Spatial modelling of soil microbial interactions and the emergence of purely spatial interactions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11046, https://doi.org/10.5194/egusphere-egu26-11046, 2026.
The application of molecular techniques in analysing aerobiology and airborne environmental DNA (eDNA) has expanded rapidly in recent years, offering powerful tools for indirect detection of plant, animal, and microbial taxa at landscape scale. Monitoring shifts in plant communities in response to human activity or management actions is crucial to understand their impact on biodiversity. To date, most airborne DNA studies have focused on pollen and single species detection, overlooking a variety of aerobiological sources, including plant fragments. Consequently, surveying entire plant communities through DNA metabarcoding is increasingly utilised, as it has the potential to enhance detection accuracy and broaden ecological insights at a landscape scale.
Here, we present how a passive air sampler and DNA metabarcoding can be employed to characterise plant biodiversity by capturing aerobiological material. Samplers were deployed across woodland and grassland habitats, with weekly collections used to characterise local plant community composition and quantify temporal dynamics in species detection. Aerobiological material collected by the samplers were analysed using plant-targeted DNA markers and sequenced on the Oxford Nanopore Technologies MinION platform. To evaluate methodological robustness, a sampler was positioned adjacent to a standard pollen trap, enabling comparison of taxa recovered by molecular and morphological methods.
Temporal and spatial patterns revealed through traditional pollen microscopy were closely aligned with those obtained via our molecular workflow, with the DNA based method providing finer taxonomic resolution. Although three days of deployment yielded sufficient cellular material for aerobiological analysis, we recommend a minimum of six days to reliably capture full community composition. Overall, our results demonstrate that aerobiological DNA metabarcoding is a scalable and sensitive approach for characterising plant communities and provides a powerful compliment to existing biodiversity and pollen monitoring programmes.
Integrating environmental genomics with established, aerobiological surveillance methods offer substantial advantages, including the detection of non-pollen plant material and the early recognition of non-native or potentially invasive species. We see considerable potential in combining environmental genomics with existing airborne monitoring approaches. The portability of the MinION device enables metabarcoding directly at the point of sampling, reducing transport delays and minimizing sample degradation, and is especially valuable in biodiversity-rich but under-resourced areas, where timely aerobiological data can guide conservation decisions and support early detection of invasive species.
How to cite: Alathari, S.: In-field monitoring of airborne biodiversity using a passive sampler , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3377, https://doi.org/10.5194/egusphere-egu26-3377, 2026.
Uncertainty in the dynamics of Amazon rainforest poses a critical challenge for accurately modeling the global carbon cycle. Current dynamic global vegetation models (DGVMs), which use one or two plant functional types for tropical rainforests, fail to capture observed biomass and mortality gradients in this region, raising concerns about their ability to predict forest responses to global change drivers. Here we assess the importance of spatially varying parameters to resolve ecosystem spatial heterogeneity in the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) DGVM. Using satellite observations of tree aboveground biomass (AGB), gross primary productivity (GPP), and biomass mortality rates, we optimized two key parameters: the alpha self-thinning (α), which controls tree mortality induced by light competition, and the nitrogen use efficiency of photosynthesis (η), which regulates GPP. The model incorporating spatially optimized α and η parameters successfully reproduces the spatial variability of AGB (R2=0.82), GPP (R2=0.79), and biomass mortality rates (R2=0.73) when compared to remote sensing observations in intact Amazon rainforests, whereas the model using spatially constant parameters has R2 values lower than 0.04 for all observations. Furthermore, the relationships between the optimized parameters and ecosystem traits, as well as climate variables were evaluated using random forest regression. We found that wood density emerges as the most important determinant of α, which is in line with existing theory, while water deficit conditions significantly impact η. This study presents an efficient and accurate approach to enhancing the simulation of Amazonian carbon pools and fluxes in DGVMs by assimilating existing observational data, offering valuable insights for future model development and parameterization.
How to cite: Zhu, L., Ciais, P., Yao, Y., Goll, D., Luyssaert, S., Martínez Cano, I., Fendrich, A., Li, L., Yang, H., Saatchi, S., Dalagnol, R., and Li, W.: Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7161, https://doi.org/10.5194/egusphere-egu26-7161, 2026.
Coral reef ecosystems are highly sensitive to environmental change, and their long-term persistence depends in part on flexible feeding strategies and symbiotic associations. A well-documented example of a major environmental perturbation is the progressive closure of the Isthmus of Panama during the Pliocene epoch (ca. 4.6–4.1 Ma), which initiated the transformation of the Caribbean Sea from a relatively nutrient-rich to a more oligotrophic marine environment. This reorganization imposed strong selective pressures on reef organisms, particularly corals, to adapt to declining nutrient availability.
Fossil records indicate that many modern Caribbean coral taxa originated before the Pliocene–Pleistocene transition. It remains unclear whether these species had already developed strong host-endosymbiont nutrient coupling prior to the closure of the Isthmus or whether these traits evolved in response to it. Here, we investigate this question by analyzing stable isotope records from fossil corals spanning the Late Miocene to the present in the Caribbean Sea. Coral-bound nitrogen isotope ratios (CB-δ15N) are used to infer changes in internal nitrogen recycling and host-endosymbiont coupling, while coral-bound oxygen isotope ratios (CB-δ18O) provide constraints on past seawater temperatures.
We hypothesize that many coral lineages had already developed tighter host-endosymbiont nutrient coupling before the Isthmus closure, and that species with intermediate levels of symbiosis facilitated adaption to more oligotrophic condition. This pre-adaptation may explain both the successful establishment of the modern Caribbean coral fauna after the closure and its present-day vulnerability to rapid anthropogenic stressors such as warming and nutrient pollution. By placing modern reef ecology in an evolutionary and paleoenvironmental context, this study aims to improve our understanding of coral resilience and inform future conservation strategies.
How to cite: Schröder, J., Jung, J., Martongelli, I., O´Dea, A., Klaus, J., Gischler, E., Vonhof, H., Sigman, D. M., Brachert, T., Haug, G. H., and Martinez-Garcia, A.: Reconstructing the Strength of Photosynthetic Endosymbiosis in Caribbean Corals before the Closure of the Isthmus of Panama, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7805, https://doi.org/10.5194/egusphere-egu26-7805, 2026.
Soil represents the largest terrestrial carbon sink, with a substantial fraction stored in subsoils. Microbial functional diversity regulates ecosystem carbon cycling, yet how microbial traits vary with soil depth and landscape transitions remain poorly understood. This knowledge gap is particularly relevant in coastal environments, where hydrologic and biogeochemical gradients impose strong selective pressures on microbial metabolism. We investigated microbial functional diversity and carbon utilization patterns across a coastal forest–salt marsh gradient, with a specific focus on depth-resolved trait expression and biogeochemical consequences. Monthly in situ porewater sampling was conducted across forest, wetland, and creek environments, from surface soils to subsurface layers. Porewater chemistry (pH, redox potential, electrical conductivity, dissolved organic carbon, DOC) was monitored to characterize environmental and biogeochemical gradients. Microbial carbon utilization patterns and functional diversity were assessed using Biolog EcoPlates, and key extracellular enzyme activities (β-glucosidase and phosphatase) were measured to evaluate microbial activity. DOC concentration increased from forest to wetland soils, accompanied by shifts in microbial functional traits. Forest soils, wetland surface layers and creek samples supported higher microbial diversity, whereas wetland deep layers retained a strong metabolic capacity for processing complex organic carbon substrates, indicating functional specialization under persistent anoxic and saline conditions. Deep layers showed measurable enzyme activities, indicating active microbial carbon turnover. These findings demonstrate that microbial functional diversity varies across both depth and landscape gradients, with implications for carbon transformation and storage in coastal ecosystems.
How to cite: Liu, Y. and Jin, Y.: Depth-Resolved Microbial Functional Diversity and Carbon Utilization Across a Forest–Wetland Gradient, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8315, https://doi.org/10.5194/egusphere-egu26-8315, 2026.
As alpine glaciers recede with global warming, proglacial forefields expand, and the processes of soil development take hold. The ecosystem transition toward greening is thought to be initiated by microorganisms that exert biotic weathering forces and accumulate carbon and nitrogen. A portion of the glacial sediments involved in this transition are classified as glacial rock flour. Glacial rock flour’s small particle size and large surface area suggest that it may offer a preferrable habitat and source of inorganic nutrients for microorganisms. However, microbial communities in glacial rock flour have yet to be reported. To investigate the microbial communities that colonize glacial rock flour, deposits were sampled near the melt streams of Mer de Glace and Glacier d’Argentière (Mont Blanc Massif, France). These glaciers flow over largely granitic bedrock. At both sites, three sampling points were selected with increasing distance from the glacier. At Glacier d’Argentière, three subglacial samples were collected off the basal ice surface. We hypothesized that characteristics of the glacial rock flour, such as median grain size or sampling distance from the glacier, would influence alpha diversity and abundance of the prokaryotic community. Laser particle size analysis, X-ray Diffraction (XRD), and geochemical extractions were completed to characterise the material. Quantitative polymerase chain reaction (qPCR) targeting the 16S rRNA gene and metabarcoding of the v3-v4 region of the 16S rRNA gene (rrs) were completed on DNA extracts to estimate prokaryotic abundance, probe taxonomic differences, and compute alpha diversity indices. A prokaryotic community was detected in all samples with a negative correlation evident between median particle size and prokaryotic abundance. Prokaryotic alpha diversity indices (Chao1, Shannon, Simpson) suggest that subglacial alpha diversity is greater than proglacial forefield alpha diversity. However, prokaryotes were less abundant in subglacial samples compared to proglacial samples. These results represent the first report of microbial communities in subglacial and proglacial glacial rock flour sediment.
How to cite: Sampsell, K., Köhler, K., Schivalocchi, F., Diadkina, E., Denis, H., Wild, B., Vogel, T. M., and Larose, C.: Subglacial and proglacial microbial communities in glacial rock flour of the Mont Blanc Massif, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10585, https://doi.org/10.5194/egusphere-egu26-10585, 2026.
Bryophytes and lichens in permafrost regions act as a natural insulation cover and thus cool underlying soil layers due to their porous, air-filled structure. The retained water content varies in response to evapotranspiration and freeze-thaw transitions, thereby modulating the insulation effects. Climate change is driving alterations in functional diversity of these highly sensitive non-vascular vegetation communities. Shifts in functional traits are closely linked to height and water retention capacity, thus the insulating properties, of the bryophyte and lichen layer. However, it is largely unclear how changes in functional diversity of non-vascular vegetation will affect soil temperature. Yet this gap may be addressed by trait-based models that simulate the mutual interaction between biodiversity and soil state.
This study focuses on bryophyte and lichen vegetation in high-latitude permafrost ecosystems, aiming to: (1) quantify their insulation effects on soil temperature under long-term climate change, and (2) clarify the underlying mechanisms by which functional diversity modulates the insulation effects. To this end, we refine the permafrost processes within the trait- and process- based LiBry model to accurately capture the coupled states of soil and diversity. Model experiments to isolate effects of bryophyte and lichen vegetation are implemented to determine their contribution to soil temperature variations. We further drive the model with a gradient of climate and diversity scenarios to reveal the relationships between distribution of functional traits and insulation effects. Our findings contribute to a more comprehensive understanding of the impacts of functional diversity on key permafrost processes in data-scarce contexts.
How to cite: Zhu, Y. and Porada, P.: Uncover the link between functional trait diversity and thermal insulation effects of bryophytes and lichens in permafrost regions: Insights from a processed-based model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12845, https://doi.org/10.5194/egusphere-egu26-12845, 2026.
Equilibrated soil organic carbon (SOC) states are a prerequisite for Earth system model simulations following CMIP and TRENDY protocols, which rely on long preindustrial spin-up phases prior to historical and future integrations. While conventional linear soil carbon models readily achieve equilibrium, microbial-explicit soil carbon models frequently exhibit slow convergence or persistent SOC drift even after millennial-scale spin-up, raising concerns about their applicability in Earth system simulations.
Previous analytical work has derived steady-state solutions for microbial soil carbon models under the assumption of vertically integrated, well-mixed systems, but it remains unclear whether such analytical equilibria are sufficient when models include vertical soil structure and transport processes. Here, we systematically assess the role of soil profile discretization, transport, and model structure in controlling SOC equilibration, and evaluate whether analytically derived steady states can provide reliable initial conditions for depth-resolved microbial soil carbon models.
Using the QUINCY land model framework, we conduct a hierarchy of simulations under standard CMIP-style protocols, consisting of a 1000-year spin-up followed by historical simulations (1850–2019). First, we apply QUINCY-derived litter inputs to the vertically integrated microbial soil carbon model Millennial, which includes explicit microbial dynamics and mineral-associated organic matter formation but no vertical transport. Second, we simulate soil carbon dynamics in QUINCY using a CENTURY-type linear soil model (SSM) with explicit vertical discretization (5 and 15 soil layers to 9.5 m depth), providing a reference case with well-defined analytical equilibria. Third, we perform fully depth-resolved simulations using the Jena Soil Model (JSM) within QUINCY, combining microbial-explicit carbon cycling, sorption dynamics, and vertical transport.
We hypothesize that difficulties in equilibrating microbial soil carbon models arise primarily from structural interactions between nonlinear microbial kinetics, sorption capacity constraints, and vertical transport, rather than from numerical deficiencies or insufficient spin-up duration. We further expect that analytically constrained initial conditions substantially reduce equilibration times and SOC drift in bucket models and linear depth-resolved systems, while providing a useful—but not fully sufficient—approximation for initializing complex microbial soil carbon models with dynamic soil profiles.
By explicitly comparing linear and microbial soil carbon models across vertically integrated and depth-resolved configurations, this study clarifies the conditions under which analytical steady-state solutions are adequate for CMIP- and TRENDY-style simulations, and identifies remaining structural challenges for deploying microbial soil carbon models in Earth system frameworks.
How to cite: Yu, L., Liu, J., Wu, H., Gong, C., Kwon, M., Rodriguez, X., Zaehle, S., and Beer, C.: Soil profile structure and transport control equilibrium in microbial soil carbon models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17497, https://doi.org/10.5194/egusphere-egu26-17497, 2026.
Soil salinization threatens agricultural production and wetland functioning in coastal deltas. This threat is expected to intensify with climate change because increasing evapotranspiration, decreasing fresh water supply from rivers, and sea-level rise will expand salt influence into low-lying areas. In such settings, shallow brackish groundwater, evapotranspiration, and land-use–specific hydrology interacts across subtle topographic gradients, with yet unconstrained consequences for both salinity levels and sodicity risk. In this study, we quantified the combined effects of elevation, land use, and soil depth in soils of the Camargue (southern France), a multifunctional delta dominated by paddy rice, dry agriculture (e.g., wheat, clover) and pastureland.
At three elevation classes (low = 0.2–0.6 m a.s.l.; mid = 0.6–1.0 m a.s.l.; high = 1.0–1.4 m a.s.l.), we collected 362 soil cores by manual drilling (using a 1-m auger), which were subdivided into five 20-cm soil-depth increments (0–20, 20–40, 40–60, 60–80, 80–100 cm). We used 1:5 soil:water extracts to measure electrical conductivity (EC) and a targeted ion suite [mg L⁻¹; meq L⁻¹]. We derived dissolved salts (DS = sum of quantified ions), Na-dominance-ratio (Na⁺/√[(Ca²⁺+Mg²⁺)/2]), and a Na⁺–Cl⁻ imbalance metric (ΔNa⁺ = Na⁺ − Cl⁻ [meq L⁻¹]) to distinguish Na⁺–Cl⁻ dominance from Na⁺ enrichment decoupled from Cl⁻.
EC and DS generally increased towards the lower elevations and with soil depth, indicating salt accumulation where drainage is constrained and groundwater influence is strongest. These elevation trends were most pronounced in soils under paddy rice and pastureland (rice median EC = 0.44–0.97 mS cm⁻¹; DS = 306–642 mg L⁻¹; pasture median EC = 0.90–1.97 mS cm⁻¹; DS = 502–942 mg L⁻¹). Soils under dry agriculture showed a different pattern (EC = 0.27–0.49 mS cm⁻¹; DS = 236–314 mg L⁻¹) toward lower elevations. Ion composition was dominated by Na⁺ (20%) > Cl⁻ (18%) > K⁺ (17.7%) > SO₄²⁻ (16%) > NO₃⁻ (10.6%) > Ca²⁺ (4.7%) > Mg²⁺ (0.6%). ΔNa was predominantly positive, especially in soils under paddy rice, coinciding with elevated Na-dominance-ratio (3.4–12.7), indicating widespread Na⁺ excess relative to Cl⁻ and suggesting potential sodicity risk. Negative ΔNa⁺ values occurred mainly in some pasturelands (−0.15 to −8.7 meq L⁻¹), consistent with Cl⁻-dominant inputs (e.g., sea salts, fertilizers).
Projected increases in evapotranspiration and sea-level rise under global warming are likely to reduce arable land availability in the Camargue, suggesting a heightened vulnerability to combined salinity–sodicity pressures. Specifically, to maintain rice cultivation along with all its cultural heritage for the people in the Camargue, a sustained effort for freshwater irrigation and effective drainage needs to be prioritized.
How to cite: Asabere, S. B., Hielscher, I., Regis, J., Lourenco, M., Boutron, O., and Sauer, D.: Soil salinity and sodicity in the Camargue (Rhône river delta, France) are strongly controlled by elevation, land use, soil depth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20949, https://doi.org/10.5194/egusphere-egu26-20949, 2026.
Cyanobacteria play a significant role in global biogeochemical cycles, including carbon fixation and oxygen production. Among them, marine picocyanobacteria Prochlorococcus and Synechococcus constitute the most numerically abundant group of photosynthetic organisms on Earth. They are dominant in oligotrophic regions and contribute a quarter of primary production in the ocean. Picocyanobacterial distribution depends on abiotic factors, e.g. light, temperature, and nutrients, as well as biotic mortality factors, such as grazers and viral infection. Viruses also impact the diversity of picocyanobacteria during their coevolution. Infection of cyanobacteria by phages ends in lysis and release of organic matter from cells to the water column. The T7-like cyanophage family is one of two main virus families infecting marine picocyanobacteria. Two groups of T7-like cyanophages were known until recently: clades A and B. They have various distribution, infection properties and patterns, resulting in differential impacts on picocyanobacterial populations. In 2023 a new group of T7-like cyanophages was discovered, and was named clade C. However, only two genotypes of the novel group were known, both isolated on Prochlorococcus. In this study we investigated the diversity within the new group using assembled environmental sequences. We also estimated the relative abundance and infection of this group and compared them with other T7-like cyanophages clades along a transect in the North Pacific Ocean and over the spring period or from winter mixing to summer stratification in the Red Sea. For this we used viromic and cellular metagenomic data to determine relative abundance of free-living viruses and gain an indication of infection, respectively. We found that the new group actually consists of two distinct clades, which we renamed as clades C and D. Clade D is more diverse than clade C. In the North Pacific Ocean both clades were relatively more abundant in the North Pacific Subtropical Gyre and decreased towards the north. In some samples clade D recruited more than 40% of T7-like cyanophage viromic reads. In the Red Sea the relative abundance of both clades increased towards the summer. In both regions clade D was generally more abundant that clade C, and the abundances of clades C and D followed the abundances of Prochlorococcus. This study provides new insights into the diversity, spatial distribution and seasonal dynamics of two new clades of T7-like cyanophages. It demonstrates that clade D could be an important viral group impacting primary production and biogeochemical cycles in the oligotrophic oceans.
How to cite: Khavin, E., Kondratieva, K., Maidanik, I., Carlson, M., Perkarsky, I., and Lindell, D.: Two new clades of T7-like cyanophages: diversity, distribution and infection patterns based on omics data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-824, https://doi.org/10.5194/egusphere-egu26-824, 2026.
Plant hydraulic traits regulate water transport in the soil–plant–atmosphere continuum and mediate the coupling between soil moisture availability, stomatal regulation, and ecosystem carbon uptake. Mechanistic representations of plant hydraulics in land surface models, such as the Community Land Model (CLM), improve the accuracy of simulated vegetation fluxes, particularly under drying soil conditions¹, but they also introduce additional parameters that can be difficult to constrain and can strongly influence model outputs. Global ensemble perturbation experiments in CLM have shown that plant hydraulic parameters are among the most influential controls on evapotranspiration, although their relative importance varies across regions². Yet, how the sensitivity of these parameters varies across plant functional types (PFTs) and seasons remains largely unexplored.
In this study, we investigated the sensitivity of simulated vegetation water potential and water and carbon fluxes to five key plant hydraulic parameters, including stomatal behavior (medlyn slope), plant and root conductance (kmax and krmax), cavitation resistance (psi50) and root distribution (β) using eCLM (https://github.com/HPSCTerrSys/eCLM). Ensemble simulations were performed for 13 ICOS sites across Europe, covering four climate zones and five PFTs, over the period 2009–2018. The selected parameters were varied within PFT-dependent ranges following previous perturbation experiments²,³, resulting in a total of 336 ensemble members. Variance-based parameter sensitivities (main effects, two-way interaction effects, and total effects) were quantified using the GEM-SA global sensitivity analysis framework based on Gaussian process emulation4. Emulators were trained on monthly averages for each station and each output variable individually.
Across simulations, medlyn slope and kmax showed the strongest effects on simulated water and carbon fluxes (ET, Tr, GPP, NEE) with main effects explaining more than 60% of the variance, while two-way interaction effects contributed only marginally. However, parameter sensitivities varied substantially among PFTs, with distinct patterns in the relative importance of dominant parameters for Mediterranean evergreen broadleaf forests, temperate deciduous forests, and evergreen needleleaf forests. Sensitivities also varied seasonally, with the remaining parameters—particularly psi₅₀—becoming increasingly influential under dry summer conditions. Most notably, seasonal shifts in the direction of parameter effects on canopy transpiration were detected at drought-prone Mediterranean sites: higher medlyn slope increased transpiration during spring, but led to reduced transpiration during summer, reflecting earlier stomatal closure under increasing plant hydraulic stress.
Our results show that model sensitivity to plant hydraulic parameters varies across PFTs and seasons, reflecting changes in model behavior across environments. These findings motivate further model development and refinement of plant hydraulic and stomatal process representation to ensure consistent performance across seasons, especially during drought.
References
- 1Kennedy et al. (2019). 10.1029/2018MS001500
- 2Kennedy et al. (2025). 10.1029/2024MS004715
- 3Eloundou et al. (2024). 10.5194/egusphere-egu24-16086
- 4O’Hagan (2006). 10.1016/j.ress.2005.11.025
How to cite: Baca Cabrera, J. C., Eloundou, F., Naz, B. S., Poppe Terán, C., Hendricks Franssen, H.-J., and Vanderborght, J.: Seasonal shifts in the sensitivity of plant hydraulic parameters controlling ecosystem water and carbon fluxes in eCLM, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3390, https://doi.org/10.5194/egusphere-egu26-3390, 2026.
Water-use efficiency (WUE) quantifies the ratio of CO₂ assimilation to transpiration, reflecting the trade-off between carbon gain and water loss. It therefore provides key information about ecosystems’ strategies for dealing with drought, as well as their responses and feedbacks to climate. From an optimality perspective, a robust theory to predict WUE is fundamental for exploring potential adaptations, shifts in vegetation communities, or migration, especially under future scenarios.
Global estimates of WUE, generated by terrestrial biosphere models (TBMs), typically evaluate the accuracy of their predictions using observed fluxes. However, these evaluations often overlook whether the simulated sensitivity of fluxes to environmental drivers matches observed sensitivities, possibly covering flaws in the underlying theory, allowing models to produce “right answers for the wrong reasons”.
Here, we assess the sensitivity of WUE simulated by the TRENDY models to environmental variables and compare them against sensitivities inferred from δ¹³C isotopes and state-of-the-art remotely sensed datasets derived from machine learning. We found qualitative disagreements (opposite signs) in the sensitivity coefficients of WUE to environmental variables, highlighting gaps in the current theoretical understanding of ecosystem functioning.
How to cite: Sandoval, D., Orme, D., and Prentice, I. C.: Right answers for the wrong Reasons? Testing water use efficiency responses in terrestrial biosphere models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5874, https://doi.org/10.5194/egusphere-egu26-5874, 2026.
A recurring challenge in ecosystem science is modeling the variance of biogeochemical process rates in connection with local microbial community composition. Mechanistic models usually relies on fixed parameters that ignore such ecological variations. Purely statistical approaches require extensive data and, lacking process-based information, often overfit to training conditions, limiting their ability to generalize. We present here a hybrid modeling framework that combines these approaches, allowing mechanistic biogeochemical models to adapt their parameters based on local microbial community structure.
Our approach uses neural networks to translate microbial community composition (bacterial and fungal taxonomic data) into site-specific key parameters in a mechanistic carbon-nitrogen cycling equations. Since these intermediate parameters likely capture multiple processes, we view them as functional parameters that allow the mechanistic model to flexibly incorporate the variance of decomposition rates due to local microbial communities, while still maintaining the interpretable structure of process-based equations and retaining the deterministic information for the processes we know how to model.
The innovation lies in including community composition from sequencing directly as a driver of parameter variation within established biogeochemical theory, preserving information that would otherwise be lost (for example assembling the sequencing data into diversity indicators). Literature-derived constraints ensure parameters remain within physically plausible ranges, but the neural components learns how microbial community structure modulates these values locally to improve predictions.
This methodological framework demonstrates that we can link communities with decomposition processes without requiring a complete mechanistic understanding (with consequent biases due to likely missing processes) of every intermediate step. This approach is broadly applicable, solving the difficulties coming from knowing that functional diversity influences biogeochemical processes but with an incomplete understanding of all the underlying mechanistic complexity, embedding the paradigm of soil decomposition kinetics as emergent ecological properties rather than as fixed intrinsic characteristics.
How to cite: Menichetti, L., Bruni, E., Ahrens, B., Rossdeutscher, L., and Curiel-Juste, J.: Including microbial communities in soil carbon-nitrogen cycling modeling via a hybrid neural-mechanistic modeling approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9721, https://doi.org/10.5194/egusphere-egu26-9721, 2026.
Mesoscale eddies are key oceanographic features influencing zooplankton community structure and ecosystem function. However, the vertical impacts of cyclonic and anticyclonic eddies on zooplankton energy transfer efficiency remain unclear in the northern South China Sea (SCS). We conducted a field survey in April 2023, collecting zooplankton samples with a multi-net system and analyzing them via ZooScan imaging technology. Size-based and trophic indicators—including the normalized biovolume size spectrum (NBSS), size diversity, and average equivalent spherical diameter (ESD)—were used to assess energy transfer efficiency across depth layers and eddy types. Results indicated significantly higher zooplankton total abundance, biovolume, and carbon biomass within cyclonic eddies (mean ± SD: 93.2 ± 25.7 ind./m3, 45.4±20.9 mm3/m3, 2.9±1.5 mg C/m3) compared to anticyclonic eddies (mean ± SD: 82.2±23.0 ind./m3, 37.8±14.0 mm3/m3, 2.4±0.9 mg C/m3) in the upper 300 m. Small copepods dominated all depth layers in both eddy types, comprising over 70% of the total abundance. Functional indicators, including the NBSS slope, size diversity, and average ESD, indicated higher energy transfer efficiency in cyclonic eddies within the upper 300 m. However, at the 0–25 m depth layers, anticyclonic eddies exhibited flatter NBSS slopes and higher size diversity than cyclonic eddies. Zooplankton productivity declined consistently with depth, while energy transfer efficiency to higher trophic levels showed a fluctuating vertical pattern and tended to rebound in deeper layers. Our findings highlight the crucial role of mesoscale eddy dynamics in structuring zooplankton communities and regulating energy flow in pelagic ecosystems of the northern SCS.
How to cite: Wang, S., Zhang, F., Chi, X., Li, Q., Zang, W., and Sun, S.: Zooplankton Size Structure and Energy Transfer Characteristics under the Influence of Mesoscale Eddies in the Northern South China Sea during Spring: Insights from ZooScan Imaging , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6255, https://doi.org/10.5194/egusphere-egu26-6255, 2026.
Soil moisture is a major constraint on terrestrial gross primary productivity (GPP). In this study, we propose and test two hypotheses to explain how soil moisture limits carbon uptake: 1) plants reduce stomatal conductance around midday to conserve water, leading to a temporary decline in internal CO₂ concentration and photosynthesis; and 2) water stress causes a more general reduction in photosynthetic capacity, expressed as a decrease in the quantum efficiency of photosynthesis (φ₀), thereby lowering GPP throughout the day. Here, we combine Eco-Evolutionary Optimality (EEO) Theory with eddy covariance observations to separate and quantify stomatal and non-stomatal responses of GPP to soil moisture. Our results show that both midday stomatal closure and photosynthetic capacity suppression coexist, supporting both hypotheses, with their relative importance strongly modulated by soil moisture. Across most sites, the magnitude of midday GPP depression weakens with increasing soil moisture, indicating that stomatal responses are more sensitive under low soil moisture conditions. In addition, photosynthetic capacity increases with soil moisture, contributing to an overall enhancement of daily GPP. By explicitly separating stomatal and non-stomatal pathways through which soil moisture affects carbon uptake, this study provides a mechanistic explanation for the more conservative water use strategies observed in plants from dry climates and improves the representation of diurnal GPP dynamics in water-limited ecosystems.
How to cite: Gao, M., Sandoval, D., and Prentice, I. C.: Separating stomatal and non-stomatal responses of gross primary productivity to soil moisture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11414, https://doi.org/10.5194/egusphere-egu26-11414, 2026.
Soils support a wide range of ecosystem functions and services, including climate regulation, nutrient cycling and carbon sequestration. Most of these functions are strongly impacted by a large diversity of microorganisms hosted in soil (e.g., bacteria, fungi) which are increasingly threatened by human-induced global change factors such as climate warming or land-use change. A deep understanding of how microbial communities function is thus crucial to evaluate how they influence ecosystem services but also how anthropogenic perturbation may affects soil quality and the delivery of these services. While great efforts have been made to evaluate the relationships between microbial diversity and ecosystem functions, much less attention has been paid to the metabolomic profiling of soil microbial communities. However, recent advances in mass spectrometry and big data processing now allow us to measure hundreds of known and unknown metabolite features constituting the soil metabolome, which can mirror the key biological processes occurring below-ground, and present an important opportunity to better understand the microbial characteristics and metabolic pathways driving soil ecosystem functions.
In this study, soil metabolic profiles and microbial communities were explored on 25 European soils from different biomes and land use types alongside soil physical and chemical measurements in order 1) to characterise soil metabolomes across a large range of soil types, 2) to investigate the links between soil microbial communities and associated metabolic profiles, and 3) to evaluate the potential of soil metabolomics to predict ecosystem functions such as soil gas exchange.
Soil metabolic profiles were screened using UHPLC-LTQ-Orbitrap mass spectrometry (LC-MS) and showed a strong gradient across sites alongside bacterial and fungal community shifts characterised using metabarcoding. The ability of soil metabolic profiles and microbial communities to predict soil ecosystem functions was evaluated through machine learning models across biomes and the interconnection of a core set of metabolic features and microbial genus was further investigated to deepen our understanding of the potential mechanisms and microbial communities involved.
How to cite: Guzman, T., Mondy, S., Kaisermann, A., Jones, S. P., Sauze, J., van Scheik, E., Wohl, S., Marcellin, K., Petriacq, P., Ogée, J., and Wingate, L.: Exploring the potential of soil metabolic and microbial composition in predicting ecosystem functions across biomes and land use types., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11850, https://doi.org/10.5194/egusphere-egu26-11850, 2026.
Background and aims
Highly pigmented extremophilic algae communities, “Blood Snow”, accelerate the retreat of glaciers and snowcaps by depressing the reflectivity of surfaces by up to 13%. In the European Alps these snow algae interact with depositions of Saharan dust, a plausible source of vital nutrients. Using a novel ML-based remote-sensing algorithm, we have tracked blooms and dust deposition events in the Alps, but we now seek molecular-level insight to better understand how, where and when these blooms occur. Unculturable key strains, remote field-sites and low biomass per unit volume has kept meta-omic analysis of functional microbial ecology impractical in these ecosystems. Standard sampling techniques require cryogens or expensive, heavy and limited portable freezers to preserve protein for multi-omics: These are, at minimum, logistically challenging if not unobtainable in remote locations. We aimed to develop ambient temperature concentration, fixation and transportation of field samples for meta-omics, expanding the ability of researchers to probe the ecology of remote extremophile communities in-situ. Better in-vitro understanding of these significant unconstrained cryospheric effects may help untangle the interactions, behavior and uncertain future of these phenomena.
Methods
Traditional flash-freezing requires the sourcing and transportation of cryogens to preserve samples as-is. Using cryogens in remote locations is hazardous, and results in bulky samples that must reach a freezer within hours. Another approach is to use in-situ concentration followed by macromolecule fixation with broad-spectrum enzyme inhibitors. This allows preservation of approximately equal quality to LN2, concentrated samples, safer fieldwork and a generous timescale for samples to reach long-term storage. This then fed into a SP3 proteomic and WGS metagenomic pipeline to identify proteins and infer what the community is capable of on its own, and what must be outsourced.
Results
We show that quality DNA and Protein can be extracted from samples gathered in this manner and present preliminary meta-omic analysis of the same, synthesised with the results of our whole Alp survey of Algal Blooms and dust deposition events. This method also solves an adjacent problem: the low biomass per volume of remote extremophiles via in-situ concentration. We will also discuss how these molecular-level insights may provide clues into community functioning, interaction with other geophysical cycles such as Saharan dust circulation, and outline future opportunities.
Conclusion
A novel sampling technique allows meta-omic exploration of microbial ecological dynamics in remote locations without cryogens. This lower barrier to entry enables affordable, compact, time-insensitive, meta-omics in remote microbial ecosystems, helping to sidestep issues in understanding these currently unculturable but highly influential organisms.
How to cite: Richardson, L., Bryant, R., Pandhal, J., Sole, A., Tallantire, F., and Swift, D.: From molecules to mountain ranges: Remote sensing of extremophilic algae blooms, Saharan dust deposition events, and meta-omic analysis of the bloom community, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13746, https://doi.org/10.5194/egusphere-egu26-13746, 2026.
Transition metals are crucial for microbial metabolism, serving as catalytic cofactors in many enzymes and contributing to protein folding. Their fluctuating bioavailability, depending on environmental concentrations and redox state, but also their potential toxicity due to high reactivity, selected for tight metal homeostasis regulation. Metal transporters lie at the core of this homeostatic control. Accordingly, microorganisms have evolved a wide diversity of metal transport systems to cope with changing environmental metal availability throughout Earth history.
The present study aims at describing the diversity and distribution of microbial metal transport systems across several geothermal environments, with a specific focus on shallow water hydrothermal vents and terrestrial deeply sourced seeps. In these ecosystems, microbial diversity and metabolism are tightly linked to the elements supplied by water-rock interactions, providing an excellent model to investigate the diversity of microbial metal transport systems.
We performed shotgun metagenomics of geofluids from more than 200 thermal features globally distributed and carried out functional annotation of sequencing reads using a manually curated database of metal transport genes. Metagenomic data were coupled to high-resolution geochemical analysis, including ion chromatography and inductively-coupled plasma mass spectrometry.
Our results reveal that microbial metal transport systems are strongly structured by geochemical context and dissolved metal availability across geothermal environments. Transporter diversity and abundance varied systematically across tectonic settings and physicochemical gradients, with metal-poor environments exhibiting higher diversity and abundance of uptake systems, whereas metal-rich and acidic environments display reduced transporter diversity and a relative enrichment of efflux-related functions. These relationships point to a dynamic regulatory mechanism, where microorganisms may adapt their metal uptake strategies in response to fluctuating metal concentrations, providing new insights into microbial evolution of metal transport systems. Such findings could have broader implications for understanding microbial evolution in extreme environments, providing more insights into the fundamental role of metal availability in the regulation of microbial diversity.
How to cite: Migliaccio, F., Corso, D., Cascone, M., Taccaliti, E., De Pins, B., Bastoni, D., Selci, M., Gallo, G., Bastianoni, A., Di Iorio, L., Vetriani, C., Barry, P. H., Tyne, R. L., Lloyd, K. G., Jessen, G. L., Chiodi, A., De Moor, M. J., Ramirez, C. J., Cordone, A., and Giovannelli, D. and the Giovannelli Lab - Università degli Studi di Napoli Federico II, Department of Biology: Environmental drivers of microbial metal transporter diversity in geothermal systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13876, https://doi.org/10.5194/egusphere-egu26-13876, 2026.
Terrestrial ecosystems absorb ≈30% of anthropogenic CO2 emissions in a process termed the land sink. This process thus mitigates a large fraction of current and future climate change, and Earth’s future climate depends greatly on whether or not the land sink continues. Accumulation of soil organic carbon (SOC), is responsible for a large fraction of carbon absorbed by the land sector, yet we currently lack sufficient observational constraints on changes in SOC at the global scale. Moreover, the computational models that we rely on to simulate SOC dynamics are too complex to effectively use the limited available data, leading to very large uncertainty in their projections. To help address these challenges, we develop simple-yet-powerful statistical models of soil organic carbon degradation that use available observations of carbon turnover time and radiocarbon dating to constrain the ~10-100 year dynamics of SOC, the relevant time scale over which societies can plan for climate change. We show that these models can be independently parameterized from available data, and have predictive performance on par or exceeding state-of-the-art models, with many fewer parameters.
How to cite: Bar-On, Y. and Flamholz, A.: Statistical approaches to soil carbon dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16455, https://doi.org/10.5194/egusphere-egu26-16455, 2026.
The land surface accounts for a large share of variability in the global carbon cycle. Although increasing atmospheric CO₂ concentrations have led to higher net primary production and increased land carbon stocks, vegetation carbon stocks appear largely constant, implying that changes in land carbon are primarily driven by soil organic carbon (SOC). As SOC represents the largest active carbon pool, its dynamics are critical for land–atmosphere feedbacks. However, strong spatial heterogeneity and measurement limitations result in sparse and mostly static SOC data, complicating the identification of dominant processes.
Recent studies address this limitation by assimilating soil carbon models to spatial SOC and covariate datasets using neural networks (hybrid modeling). The resulting spatial parameter fields are then interpreted in terms of underlying mechanisms. These approaches typically rely on three key assumptions: steady-state conditions, adequate process representation by the assimilated SOC model, and the sufficiency of bulk SOC data to infer processes. In this study, we explicitly test these assumptions.
We use the Europe-wide LUCAS dataset, which provides spatially resolved physical and chemical soil data at multiple time points. A subset of the dataset includes SOC subfractions, including mineral-associated organic carbon and microbial biomass carbon. Several simple SOC models were assimilated in their steady-state form in the hybrid framework, while accounting for differences in model flexibility. This allowed exclusion of specific modeling assumptions. Comparisons across time steps were used to assess the validity of the steady-state assumption. In addition, first results obtained with a dynamic SOC model are presented.
How to cite: Roßdeutscher, L., Georgiou, K., Riley, W., Reichstein, M., Schrumpf, M., Wutzler, T., and Ahrens, B.: Spatial hybrid modeling of soil organic carbon processes: testing common assumptions using multivariate, dynamic data with simple models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20897, https://doi.org/10.5194/egusphere-egu26-20897, 2026.
Accurate dimensional scaling is essential for translating forest inventory measurements of stem diameter and height into estimates of tree volume, biomass, and carbon stocks, which underpin ecosystem function. Most existing scaling approaches fall into three broad classes: empirical allometries, metabolic scaling theory, and physiologically inspired models such as the pipe model. While widely used, these frameworks typically operate at coarse spatial or taxonomic scales, rely on poorly interpretable parameters, and offer limited insight into how scaling relationships vary across species and environments.
A recent parsimonious model of plant dimensional scaling is the T model, which describes tree height and crown area as a function of basal diameter. It uses just three parameters, all of which are physiologically interpretable and directly measurable functional traits. These are: (1) the initial ratio of height to diameter, or stem slenderness, which affects initial height growth rate as diameter increases, (2) maximum tree height, which affects the later saturating part of the height-diameter scaling, and (3) initial ratio of crown area to sapwood area, which is similar to the pipe model and determines the scaling of crown area with height and diameter.
Here, we combine measurements from Tallo, a large global dataset of individual tree measurements (spanning over 3000 species-site pairs) with high-resolution environmental data, to test and parameterize the T model for each species within each site. We show that: (1) The T model fits the data well, providing a parsimonious and interpretable model of plant dimensional scaling, (2) the estimated dimensional traits (i.e., the model parameters) show systematic variation across climatic gradients, suggesting an overall macroclimatic adaptation, (3) the traits exhibit substantial phenotypic plasticity, in that site-specific species-mean traits covary with environmental gradients in the same direction and magnitude as community-wide site-mean traits, (4) among coexisting species, especially in the tropics, the traits coordinate systematically with maximum height, reflecting adaptation to the light environment among different canopy strata. This systematic variation likely allows multiple trait combinations to achieve similar levels of species performance (or evolutionary fitness). Such 'functional equifinality' may provide a parsimonious explanation of biodiversity and species coexistence, complementing other known mechanisms such as niche partitioning and neutrality.
How to cite: Joshi, J., Garg, T., Hofhansl, F., Zhou, B., and Prentice, I. C.: A parsimonious and interpretable model of plant dimensional scaling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13321, https://doi.org/10.5194/egusphere-egu26-13321, 2026.
