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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Photosynthesis↗
DOE OSTI2026

Sensing Plant Photosynthesis Using Solar-Induced Chlorophyll Fluorescence: From Chloroplasts to the Globe

Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis.

ecophysiology↗
DOE OSTI2024

Tribute to Kenneth Sauer (1931–2022): a mentor, a role-model, and an inspiration to all in the field of photosynthesis

Abstract Kenneth (Ken) Sauer was a mainstay of research in photosynthesis at the University of California, Berkeley and the Lawrence Berkeley National Laboratory (LBNL) for more than 50 years. Ken will be remembered by his colleagues, and other workers in the field of photosynthesis as well, for his pioneering work that introduced the physical techniques whose application have enriched our understanding of the basic reactions of oxygenic photosynthesis. His laboratory was a training ground for many students and postdocs who went on to success in the field of photosynthesis and many others. Trained as a physical chemist, he always brought that quantitative approach to research questions and used several spectroscopic methods in his research. His broad scientific interests concerned the role of manganese in oxygen evolution, electronic properties of chlorophylls, energy transport in antenna complexes, and electron transport reactions. He was also an enthusiastic teacher, an enormously successful mentor who leaves behind a legion of scientists as his abiding legacy, a lover of music and the outdoors with many interests beyond science, and a dedicated family man with a great sense of humility. In this tribute, we summarize some aspects of Ken Sauer’s life and career, illustrated with selected research achievements, and describe his approach to research and life as we perceived it, which is complemented by reminiscences of several current researchers in photosynthesis and other fields. The supporting material includes Ken Sauers’s CV and publication list, as well as a list of the graduate students and postdocs he trained and of researchers that spent a sabbatical in his lab.

Plant Sciences↗
DOE OSTI2025

Feeding from the sun—Successes and prospects in bioengineering photosynthesis for food security

There is an urgent need for increased crop productivity to reduce food insecurity and improve sustainability. Photosynthesis converts sunlight energy into carbohydrates, providing the source of nearly all of humanity’s food. Photosynthesis is a key target for improvement, owing to inherent inefficiencies in the biochemical process. Over the last decade of advancements in bioengineering, strategies to increase the efficiency of photosynthesis were tested with proven enhancements to crop yields in field trials. Simple strategies like increasing the content of photosynthetic proteins have reliably increased photosynthesis and productivity in crops, as have more complex strategies such as bypassing photorespiration. While insertion of carbon-concentrating mechanisms into C3 plants remains an engineering challenge, modeling suggests that achieving that would have the greatest gain for crop improvement. This review discusses the many successes in improving photosynthesis achieved over the past decade and quantifies the potential for future engineering targets to increase crop productivity.

Long, Stephen P. [University of Illinois, Urbana, ↗
DOE OSTI2026

Covalent Organic Framework Photocatalysts for Water Oxidation and Overall Artificial Photosynthesis

Artificial photosynthesis through proton reduction or CO 2 reduction to generate chemical fuels has gained increasing attention as an attractive strategy for solar-to-fuel conversion. In these systems, the oxygen evolution reaction (OER) provides the necessary electrons and protons for driving the overall reaction but represents the rate-limiting step due to its inherently sluggish kinetics. Therefore, efficient overall artificial photosynthesis requires photocatalysts that can drive both the oxidation and reduction half-reactions, which impose stringent demands on catalyst design. Covalent organic frameworks (COFs) offer a versatile platform for designing such photocatalysts, owing to their strong light-harvesting capabilities , periodic architectures, and highly tunable frameworks that allow programmable catalytic sites and adjustable electronic band structures. While notable progress has been made in developing COF photocatalysts for the OER and overall artificial photosynthesis, these advances remain scattered across the literature and existing reviews, and a dedicated, systematic overview of the OER and its central role in integrated artificial photosynthetic processes is still lacking. This Review systematically summarizes recent advances in COF-based photocatalysts for the OER half-reaction and overall artificial photosynthesis. Our aim is to offer a comprehensive roadmap that establishes fundamental design principles for next-generation COF-based photocatalysts toward efficient and sustainable artificial photosynthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Atmospheric Feedbacks Reverse the Sensitivity of Modeled Photosynthesis to Stomatal Function

Stomata mediate fluxes of carbon and water between terrestrial plants and the atmosphere. These fluxes are governed by stomatal function and can be modulated in many Earth system models by an empirical parameter within the calculation of stomatal conductance, the stomatal slope (𝑔 1⁢𝑀 ). Intuitively, 𝑔 1⁢𝑀 represents the marginal water cost of carbon, relating it to the emergent plant property of water use efficiency. Observations show that 𝑔 1⁢𝑀 can range widely across and within plant types in varying environments, and this distribution of 𝑔 1⁢𝑀 is not captured within Earth system models which represent each plant type with a single 𝑔 1⁢𝑀 value. Here we examine how 𝑔 1⁢𝑀 influences photosynthesis using coupled Earth system model simulations by perturbing 𝑔 1⁢𝑀 to observed 5⁢t⁢h and 95⁢t⁢h percentiles for each plant type. We find that high 𝑔 1⁢𝑀 reduces photosynthesis nearly everywhere, while low 𝑔 1⁢𝑀 has regionally dependent responses. Under fixed atmospheric conditions, low 𝑔 1⁢𝑀 increases photosynthesis in the Amazon and central North America but decreases photosynthesis in boreal Canada. These responses reverse when the atmosphere responds interactively due to spatially differing sensitivity to increases in temperature and vapor pressure deficit. Choice of 𝑔 1⁢𝑀 also influences photosynthetic response to changes in atmospheric carbon dioxide (CO 2 ), with lower and higher 𝑔1⁢𝑀 modifying total global response to elevated 2x preindustrial CO 2 by +6.4% and −9.6%, respectively. Our work demonstrates that atmospheric feedbacks are critical for determining the photosynthetic response to 𝑔 1⁢𝑀 assumptions and some regions are particularly sensitive to choice of 𝑔 1⁢𝑀 .

Liu, Amy X. [University of Washington, Seattle, WA↗
DOE OSTI2025

Mining Thermophile Photosynthesis Genes: A Synthetic Operon Expressing Chloroflexota Species Reaction Center Genes in Rhodobacter sphaeroides

Photosynthesis is the foundation of the vast majority of life systems, and is therefore the most important bioenergetic process on earth. The greatest diversity of photosynthetic systems is found in microorganisms. However, our understanding of the biophysical and biochemical processes that transduce light into chemical energy is derived from a relatively small subset of proteins from microbes that are amenable to cultivation, in contrast to the huge number of predicted proteins that catalyze the initial photochemical reactions deposited in databases, such as from metagenomics. We describe the use of a Rhodobacter sphaeroides laboratory strain for the expression of heterologous photosynthesis genes to demonstrate the feasibility of mining this resource, focusing on hot spring Chloroflexota gene sequences. Using a synthetic operon of genes, we produced a photochemically active complex of reaction center proteins in our biological system. We also present bioinformatic analyses of anoxygenic type II reaction center sequences from metagenomic samples collected from hot (42–90 °C) springs available through the JGI IMG database, to generate a resource of diverse sequences that are potentially adapted to photosynthesis at such temperatures. These data provide a view into the natural diversity of anoxygenic photosynthesis, through a lens focused on high-temperature environments. The approach we took to express such genes can be applied for potential biotechnology purposes as well as for studies of fundamental catalytic properties of these heretofore inaccessible protein complexes.

Chloroflexota↗
DOE OSTI2026

Impact of vertical and seasonal variation in leaf traits on simulating soybean canopy photosynthesis via 1D and 3D modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax 25 ), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax 25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax 25 profile achieved comparable performance to the scheme with the measured Vcmax 25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R 2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R 2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improving leaf spring phenology modelling for temperate tree species: An integration of the Farquhar–Medlyn photosynthesis model with the optimality‐based approach

Spring leaf phenology in temperate tree species is highly sensitive to climate change and significantly affects plant photosynthetic performance, resource utilization, competition and trophic interactions, thereby impacting various ecosystem functions. Although optimality-based (OPT) approaches for modelling spring phenology are increasingly recognized, the optimal representation of the underlying principle (balancing photosynthesis gains with chilling risks) remains controversial. Here, we integrated a coupled Farquhar–Medlyn photosynthesis model into an existing OPT model, and termed the resulting model R-OPT, and evaluated its performance using the PEP725 dataset, which includes 409,144 site-species-year records from across Europe. Our results show that R-OPT outperforms both the default OPT and non-optimality-based models (e.g. the chilling-forcing trade-off and growing degree day models). This improved performance is consistent within and across five focal tree species but varies by region: R-OPT excels in lowland, moist environments but is less effective in high-altitude, cold, and dry areas, possibly due to an incomplete representation of environmental constraints on photosynthetic carbon gain in these regions. Our research advances leaf spring phenology modelling by emphasizing an optimality principle that balances photosynthetic carbon gain with chilling risk, improving the representation of plant photosynthesis processes and enhancing understanding of environmental factors influencing phenology in the context of climate change.

coupled Farquhar–Medlyn photosynthesis model↗
DOE OSTI2024

Enhancing Photosynthesis Simulation Performance in ESMs with Machine Learning-Assisted Solvers

When simulating vegetation dynamics, photosynthesis accounts for a large fraction of the computational cost in most Earth System Models (ESMs). This is largely since photosynthesis is represented as a system of nonlinear equations, and the solution requires the use of an initial guess followed by many iterations of the numerical solver to obtain a solution. We use machine learning (ML) to replicate the response surface of the model’s numerical solver to improve the choice of initial guess, therefore requiring fewer iterations to obtain a final solution. We implemented this test on the leaf-level calculations as well as at the canopy scale, and for both we observed fewer iterations of the photosynthesis solver when a ML-based initial guess was implemented. The model tested here is the Energy Exascale Earth System Model - Land Model (ELM). The ML-based algorithms used here are trained on simulations from the model itself and used only to improve the initial guess for the solver; therefore, the model maintains its own set of physics to obtain the final solution. This work shows novel ways to utilize ML-based methods to improve the performance of numerical solvers in ESMs.

Massoud, Elias [ORNL] (ORCID:0000000217725361)↗
DOE OSTI2025

Fundamental Research Aimed at Diverting Excess Reducing Power in Photosynthesis to Orthogonal Metabolic Pathways

Photosystems are incredible biological machines that use sunlight to drive the conversion of carbon dioxide to sugar. The amount of sunlight available for photosynthesis sometimes exceeds the amount of energy plants can use. This excess energy has to be safely dissipated through non-productive biological processes. The ultimate goal of this project is to understand whether we can utilize that otherwise unused excess energy. In our previous work, we showed that, in principle, it is possible to attach a catalyst to photosystem I and generate H2 using light. Our current strategy is to genetically fuse parts of the photosystem I complex with a recently discovered oxygen-tolerant [FeFe] hydrogenase. Our rationale is that such chimeric proteins may potentially result in the natural incorporation of the photosystem I-hydrogenase link using the inherent genetic machinery of the cell. In this project, we aim to verify that light-driven hydrogen production in this construction is possible. Throughout the project, we designed nanoconstructs that showcase the plausibility of this technology, at least in vitro. We take advantage of these constructs to investigate details of the coupling between photosystem I and a H2-producing enzyme called [FeFe] hydrogenase. This part of the project reveals details of the electron transfer between photosystem I and the attached hydrogenase, providing information that can lead to new strategies for improved biological photocatalysis. We also researched efficient and robust tethering of the [FeFe] hydrogenase to photosystem I in cyanobacteria. This work will highlight successful design strategies to guide the future development of photosynthetic biohybrids. Uncovering the principles governing the utilization of otherwise unusable energy significantly further our understanding of cyanobacterial photosynthesis. The work proposed establishes the feasibility of diverting excess energy under high light conditions to orthogonal enzymatic pathways and set design rules for efficient utilization of such a strategy for scientific and industrial applications in biosensing, renewable energy, and high-value chemicals production. The work addresses the DOE-BES Photosynthetic Systems program goal to develop a multidimensional understanding of photosystems that would provide specific metrics that instruct strategies for improving biological photosynthesis and for guiding the future development of bioreactors and biomimetic energy systems.

Photosynthetic systems, hydrogenase, cyanobacteria↗
DOE OSTI2025

SPRUCE Photosynthesis and Respiration of Picea mariana and Larix laricina in SPRUCE Experimental Plots, 2019

This dataset contains physiological, morphological, and chemical measurements of the two dominant coniferous species, Picea mariana and Larix laricina, in August 2019 (2019-08-20 to 2019-08-22) at the SPRUCE (Spruce and Peatland Responses under Changing Environments) experiment site in the Marcell Experimental Forest in northern Minnesota, USA. These observations help to assess the effects of whole ecosystem scale warming and elevated carbon dioxide (CO2) concentrations on peatland ecosystems. Measurements include light-saturated photosynthesis and foliar dark respiration measurements under standard conditions and growth conditions involving varying temperatures and atmospheric CO2 concentrations, as well as leaf morphology measurements (leaf mass per unit leaf area) and nitrogen content based on mass and leaf area. Net photosynthesis and dark respiration measurements were taken using portable photosynthesis systems (LI6400XT, LI6800, LI-COR Biosciences, USA). This dataset contains one data file in comma-separate values (*.csv) format. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Regulatory Coordination of Photophysical, Photochemical, and Biochemical Reactions in the Photosynthesis of Land Plants

Balance among the sequential photophysical, photochemical, and biochemical reactions of photosynthesis is needed for converting fleeting energy in light to stable energy in chemical bonds. Any imbalance acts as either a bottleneck for limiting photosynthetic efficiency or an agent for inducing structural and functional damage to photosynthetic apparatus. Not only must each reaction be carefully regulated, but regulatory processes must also be coordinated across the reactions. However, regulations of different stages of photosynthesis have rarely been studied jointly. Non-photochemical quenching (NPQ) and stomatal conductance (g s ) are key regulators of photophysical and biochemical reactions, respectively. Existing evidence suggests that the redox state of plastoquinone regulates g s and that the photochemical reactions are partially regulated by the ultrastructural dynamics of thylakoids induced by osmotic water fluxes in chloroplasts of land plants. To examine how these regulations are coordinated and feedback to each other, we simultaneously measured NPQ and gs and inferred the redox state of plastoquinone and the light-induced thylakoid swelling/shrinking on numerous C 3 and C 4 species. For all species measured, NPQ and gs covary with the redox states of the electron transport chain, particularly plastoquinone, and increase as thylakoid swelling is inferred. NPQ has the maximal sensitivity at the light intensity at which thylakoid is inferred to be fully swollen. Our findings suggest that plant energy and water use strategies are intimately linked by evolution, and studying the regulations of different photosynthetic stages as a whole can lead to new insights of the functioning of photosynthetic machinery in dynamic environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Evidence for widespread thermal acclimation of canopy photosynthesis

Plants acclimate to temperature by adjusting their photosynthetic capacity over weeks to months. However, most evidence for photosynthetic acclimation derives from leaf-scale experiments. Here we address the scarcity of evidence for canopy-scale photosynthetic acclimation by examining the correlation between maximum photosynthetic rates (A max,2,000 ) and growth temperature $(\overline{T_{air}})$ across a range of concurrent temperatures and canopy foliage quantity, using data from >200 eddy covariance sites. We detect widespread thermal acclimation of canopy-scale photosynthesis, demonstrated by enhanced A max,2,000 under higher $\overline{T_{air}}$, across flux sites with adequate water availability. A 14-day period is identified as the most relevant timescale for acclimation across all sites, with a range of 12-25 days for different plant functional types. The mean apparent thermal acclimation rate across all ecosystems is 0.41 (-0.38-1.04 for 5th-95th percentile range) µmol m -2 s -1 °C -1 , with croplands showing the largest acclimation rates and grasslands the lowest. Incorporating an optimality-based prediction of leaf photosynthetic capacities into a biochemical photosynthesis model is shown to improve the representation of thermal acclimation. Our results underscore the critical need for enhanced understanding and modelling of canopy-scale photosynthetic capacity to accurately predict plant responses to warmer growing seasons.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Nanosecond Structure of Radical Pair Intermediates from High-Frequency Quantum Oscillations: Insight into the Q A •– to Q B Electron Transfer Step in Purple Bacterial Photosynthesis

We demonstrate the validity of our approach to deduce, from the anisotropy of quantum oscillations, the geometry of short-lived radical pair intermediates in photosynthesis. A global fit of a two-dimensional W-band (94 GHz) electron paramagnetic resonance (EPR) experiment provides the same global minimum values for the geometry of the A-side radical pair P 700 •+ A 1A •− in photosystem I (PSI) as observed in a previous Q-band (34 GHz) EPR study, yet with a significantly increased convergence rate of 62%. This demonstrates that the global fit yields the correct radical pair geometry even at Q-band frequencies. With this information, we revisit our previous Q-band study of the cofactor arrangement of P 865 •+ Q A •− , the stabilized charge-separated state in purple bacterial reaction centers (RCs). Analysis of calculated two-dimensional data sets of P 865 •+ Q A •− reveals that the quantum oscillation technique is unaffected by a mirror ambiguity in disordered solids and thus can provide unambiguous solutions for all five Euler angles of the radical pair geometry. This enables us to elucidate the Q A •− to Q B electron transfer step in purple bacterial photosynthesis, the subject of controversial discussions for more than 25 years. Our results show that this electron transfer step involves a gating mechanism requiring a 60° rotation of the headgroup of Q A •− in its binding pocket.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

Net photosynthesis (A N ) is a key component of the global carbon cycle influencing climate feedback over decadal scales. Although plant acclimation to environmental changes can modify A N , traditional vegetation models in Earth system models (ESMs) often rely on plant functional type (PFT)-specific parameterizations or simplified acclimation assumptions limiting generalizability across time, space, and PFTs. In this study, we developed a differentiable photosynthesis model to learn the environmental dependencies of V c,max25 (maximum carboxylation rate at 25°C, representing photosynthetic capacity), as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of V c,max25 , learning the environment dependencies of key photosynthetic parameters improved model spatiotemporal generalizability. Applying environmental acclimation to V c,max25 led to substantial variations in global mean A N indicating the need to address acclimation in ESMs. The model effectively captured multivariate observations (V c,max25 , A N , and stomatal conductance (g s )) simultaneously with multivariate constraints, improving generalization across space and PFTs. It also learned sensible acclimation relationships of V c,max25 to different environmental conditions. The model explained more than 54%, 57%, and 62% of the variance of A N , g s , and V c,max25 , respectively, presenting a first global-scale spatial test benchmark of A N and g s . These results highlight the potential for differentiable modeling to enhance process-based modules in ESMs and effectively leverage information from large, multivariate data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Observations and modeling reveal that heatwaves reduce photosynthesis, plant carbon reserves, and net carbon uptake

Heatwaves threaten ecosystem carbon balances, yet the mechanisms driving short-term carbon flux responses remain poorly understood. Here, integrating high-frequency eddy covariance (EC) data from 140 global flux tower sites (872 site-years) with detailed process-based modeling, we examine ecosystem responses during and immediately after heatwaves. We show that heatwaves caused a −40% (range [−29%, −128%]) reduction in net ecosystem productivity (NEP) compared to pre-heatwave values, with this reduction persisting over the following two weeks (−38% range [+3%, −154%]). We attributed NEP decreases to photosynthesis decreases more than to ecosystem respiration (RE) increases. Forest sites had greater NEP decreases during heatwaves than non-forest sites, but remained carbon sinks afterwards, indicating resilience. Our modeling analysis of extreme heatwaves at selected EC sites shows that decreased photosynthesis, increased maintenance respiration, and decreased plant non-structural carbon reserves during heatwaves drive carbon cycle changes that persist for weeks. Consistent with phenocam observations, we modeled a reduction in leaf area index caused by reduced non-structural carbon reserves, leading to early leaf senescence and longer-term impacts. Ongoing increases in heatwaves are therefore likely to reduce NEP across a range of ecosystems, exacerbating carbon cycle feedback.

carbon cycle↗
DOE OSTI2024

Meta-omics reveals role of photosynthesis in microbially induced carbonate precipitation at a CO2-rich geyser

Abstract Microbially induced carbonate precipitation (MICP) is a natural process with potential biotechnological applications to address both carbon sequestration and sustainable construction needs. However, our understanding of the microbial processes involved in MICP is limited to a few well-researched pathways such as ureolytic hydrolysis. To expand our knowledge of MICP, we conducted an omics-based study on sedimentary communities from travertine around the CO2-driven Crystal Geyser near Green River, Utah. Using metagenomics and metaproteomics, we identified the community members and potential metabolic pathways involved in MICP. We found variations in microbial community composition between the two sites we sampled, but Rhodobacterales were consistently the most abundant order, including both chemoheterotrophs and anoxygenic phototrophs. We also identified several highly abundant genera of Cyanobacteriales. The dominance of these community members across both sites and the abundant presence of photosynthesis-related proteins suggest that photosynthesis could play a role in MICP at Crystal Geyser. We also found abundant bacterial proteins involved in phosphorous starvation response at both sites suggesting that P-limitation shapes both composition and function of the microbial community driving MICP.

Violette, Marlene J.↗