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At least 685 records · Page 38

Nitrogen addition alters soil fungal communities, but root fungal communities are resistant to change

Plants are colonized by numerous microorganisms serving important symbiotic functions that are vital to plant growth and success. Understanding and harnessing these interactions will be useful in both managed and natural ecosystems faced with global change, but it is still unclear how variation in environmental conditions and soils influence the trajectory of these interactions. In this study, we examine how nitrogen addition alters plant-fungal interactions within two species of Populus - Populus deltoides and P. trichocarpa. In this experiment, we manipulated plant host, starting soil (native vs. away for each tree species), and nitrogen addition in a fully factorial replicated design. After ~10 weeks of growth, we destructively harvested the plants and characterized plant growth factors and the soil and root endosphere fungal communities using targeted amplicon sequencing of the ITS2 gene region. Overall, we found nitrogen addition altered plant growth factors, e.g., plant height, chlorophyll density, and plant N content. Interestingly, nitrogen addition resulted in a lower fungal alpha diversity in soils but not plant roots. Further, there was an interactive effect of tree species, soil origin, and nitrogen addition on soil fungal community composition. Starting soils collected from Oregon and West Virginia were dominated by the ectomycorrhizal fungi Inocybe (55.8% relative abundance), but interestingly when P. deltoides was grown in its native West Virginia soil, the roots selected for a high abundance of the arbuscular mycorrhizal fungi, Rhizophagus. These results highlight the importance of soil origin and plant species on establishing plant-fungal interactions.

59 BASIC BIOLOGICAL SCIENCES↗

Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale

Abstract. This study explores the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity (GPP) across 341 catchments in the contiguous US. Seasonal climatic, hydrological, and vegetation characteristics were represented using the Horton index, ecological aridity index, evaporative fraction index, and carbon uptake efficiency. Statistical methods, including circularity statistics, correlation analysis, and causality tests, were employed to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. The results revealed a maximum lag of 2 months in the intra-annual variability of catchment water supply–productivity and atmospheric water demand–productivity relationships, with hysteresis patterns varying with the catchment's hydrological characteristics. In catchments not permanently under water-limited or energy-limited conditions, vegetation experiences hydrological stress during the peak growing period, coinciding with the highest gross primary productivity and carbon uptake efficiency being out of phase with the Horton index and in phase with the evaporative fraction index. Causality analysis highlights strong temporal continuity in GPP seasonal characteristics, with a cause–effect relationship between catchment water supply, atmospheric demand, and vegetation productivity spanning a maximum of 2 months. These findings underscore the need for a comprehensive functional framework that integrates catchment water supply, atmospheric demand, and vegetation productivity to enhance our understanding and predictive capabilities with regard to ecosystem responses to climate change.

54 ENVIRONMENTAL SCIENCES↗

Carbon Organisms Rhizosphere and Protection in Soil Environment model script and input data for soil moisture-respiration responses in tropical forests

Objectives: Climatic drying is predicted for many tropical forests, yet models remain poorly parameterized for tropical forests, hampering predictions of forest-climate feedbacks. We applied an integrated model–experiment approach, parameterizing an ecosystem model Carbon Organisms Rhizosphere and Protection in the Soil Environment (CORPSE) with tropical forest observational data, and comparing model predictions with a field drying manipulation. We hypothesized that drying would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis. We measured soil CO2 fluxes, soil moisture, soil temperature, and forest floor biomass during wet-dry cycles (2015 – 2022) in four Panamanian forests that vary in rainfall and soil fertility. We used the field data to parameterize and run tests in the model.Results: Measured CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. We used this data to parameterize the model, which then predicted increased soil CO2 fluxes in wetter and fertile forests with drying, and decreased fluxes in drier, infertile forests. In contrast to model predictions, a chronic throughfall exclusion experiment in the forests initially suppressed soil CO2 fluxes across forests, with sustained suppression after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season), as predicted by the model. The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks (results presented in Cusack et al. 2023). Datasets included: Code files:CORPSE_array.py: Defines the equations of the CORPSE modelCORPSE_solvers: Functions for running the CORPSE model using either iterative or ordinary differential equation (ODE) solversrun_Panama_sims.py: Read in datasets and run the model simulations for this studyInput data:PanamaGradientEcosystemChem_BT_CPools_20152016CO2_DC_20190615.xlsx: Plot characteristics used in running model simulationsLiCor compiled surface flux only to 2020_03 DC_20200825.xlsx: Surface gas exchange fluxes used in model-data comparisonsPARCHED litterfall data for Ben Sulman LD 20200902.xlsx: Litterfall data used to drive model simulationsInitialization data:state_500y_20190823.csv: Initial state of model pools based on previous spinup runsOutput data:Outputs/prev_moisture_response.csv: Simulations of multiple sites using original model moisture response function.Outputs/updated_moisture_response.csv: Simulations of multiple sites using updated model moisture response function.Outputs/dry15_prev_moisture_response.csv: Simulations with soil moisture reduced by 15%, using original moisture response function.Outputs/dry15_updated_moisture_response.csv: Simulations with soil moisture reduced by 15%, using updated moisture response function.Outputs/dry30_prev_moisture_response.csv: Simulations with soil moisture reduced by 30%, using original moisture response function.Outputs/dry30_updated_moisture_response.csv: Simulations with soil moisture reduced by 30%, using updated moisture response function.Outputs/latestart_prev_moisture_response.csv: Simulations with extended dry season, using original moisture response function.Outputs/latestart_updated_moisture_response.csv: Simulations with extended dry season, using updated moisture response function.Outputs/[site name]_oneyear.csv: One-year simulation for each site in expanded site list using original moisture response function.Outputs/[site name]_oneyear_dried.csv: One-year simulation for each site in expanded site list using original moisture response function, with soil moisture reduced by 25%.Outputs/[site name]_oneyear_updated_moisture_response.csv: One-year simulation for each site in expanded site list using updated moisture response function.Outputs/[site name]_oneyear_updated_moisture_response_dried.csv: One-year simulation for each site in expanded site list using updated moisture response function, with soil moisture reduced by 25%.Field plot location data:There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site).

54 ENVIRONMENTAL SCIENCES↗

Fungal community and functional responses to soil warming are greater than for soil nitrogen enrichment

Soil fungi are key regulators of forest carbon cycling and their responses to global change have effects that ripple throughout ecosystems. Global changes are expected to push many fungi beyond their environmental niches, but there are relatively few studies involving multiple, simultaneous global change factors. Here, we studied soil fungal diversity, community composition, co-occurrence patterns, and decomposition gene responses to 10 years of soil warming and nitrogen addition, alone and in combination. We specifically examined whether there were fungal community characteristics that could explain changes in soil carbon storage and organic matter chemistry in chronically warmed and fertilized soil. We found that fungal communities in warmed soils are less diverse and shift in composition. Warming also favored hyperdominance by a few mycorrhizal fungal species and lowered manganese peroxidase but increased hydrolytic enzyme encoding gene potentials. Nitrogen addition did not significantly affect fungal community composition but, like warming, did reduce fungal diversity and favored overdominance by a unique set of mycorrhizal taxa. Warming alone and in combination with nitrogen addition also reduced negative but increased positive fungal co-occurrence probabilities, promoting species coexistence. Negative fungal co-occurrence was positively correlated to soil carbon content, while the proportion of fungal hydrolytic enzyme encoding genes was negatively correlated with soil carbon content. This may reflect fungal life history trade-offs between competition (e.g., reduced negative co-occurrence) and resource acquisition (e.g., higher abundance of hydrolytic enzyme encoding genes) with implications for carbon storage.

54 ENVIRONMENTAL SCIENCES↗

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗

Long-term measurements in a mixed-grass prairie reveal a change in soil organic carbon recalcitrance and its environmental sensitivity under warming

Soil respiration, the major pathway for ecosystem carbon (C) loss, has the potential to enter a positive feedback loop with the atmospheric CO2 due to climate warming. For reliable projections of climate-carbon feedbacks, accurate quantification of soil respiration and identification of mechanisms that control its variability are essential. Process-based models simulate soil respiration as functions of belowground C input, organic matter quality, and sensitivity to environmental conditions. However, evaluation and calibration of process-based models against the long-term in situ measurements are rare. Here, we evaluate the performance of the Terrestrial ECOsystem (TECO) model in simulating total and heterotrophic soil respiration measured during a 16-year warming experiment in a mixed-grass prairie; calibrate model parameters against these and other measurements collected during the experiment; and explore whether the mechanisms of C dynamics have changed over the years. Calibrating model parameters against observations of individual years substantially improved model performance in comparison to pre-calibration simulations, explaining 79–86% of variability in observed soil respiration. Interannual variation of the calibrated model parameters indicated increasing recalcitrance of soil C and changing environmental sensitivity of microbes. Overall, we found that (1) soil organic C became more recalcitrant in intact soil compared to root-free soil; (2) warming offset the effects of increasing C recalcitrance in intact soil and changed microbial sensitivity to moisture conditions. Furthermore, these findings indicate that soil respiration may decrease in the future due to C quality, but this decrease may be offset by warming-induced changes in C cycling mechanisms and their responses to moisture conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Data for The Value of Reversible Carbon Storage in a Zero-Emissions World

Atmospheric carbon dioxide removal (CDR) is required to stabilize global temperature. CDR can be achieved via ecosystem-based approaches that are cost-effective but reversible (e.g., soil and forest management) or by more durable but expensive approaches (e.g., direct air capture coupled with geologic storage). Here, we examine trade-offs between these approaches, focusing on timing, climate impacts, and cost. We simulated reversible carbon accrual for a range of CDR contract structures using a general minimalist model of ecosystem carbon cycling, and parameterized it to simulate US agricultural soil management─specifically cover cropping─as a case study. We then quantified the resulting impact on atmospheric carbon and global temperature using a climate model emulator. We find that maintaining a patchwork of reversible CDR projects by replacing lapsed projects with new projects can reduce warming by 22–195 μ°C in 2100 and that the magnitude of this cooling effect depends on how effectively the patchwork is maintained. Long-term maintenance of reversible CDR projects requires institutional stability that cannot be guaranteed over multiple decades. Consequently, effective CDR ultimately requires replacing reversible projects with durable projects. To address this problem, we modeled the cost of replacing reversible agricultural soil CDR with geologic CDR. We found that using reversible CDR as a bridge to durable CDR is potentially more cost-effective as a global cooling strategy (0.20–0.81 billion USD per μ°C avoided) than perpetual maintenance of reversible CDR (0.32–1.31 billion USD per μ°C avoided) or an immediate transition to durable CDR (1.37–2.19 billion USD per μ°C avoided). However, we emphasize that institutional commitments to maintain reversible CDR projects cannot be guaranteed. Reliance on reversible CDR as a bridge to durable CDR therefore carries an unknown amount of risk and will only function if efforts to maintain reversible CDR are robust.

Carbon↗

Terrestrial carbon dynamics in an era of increasing wildfire

In an increasingly flammable world, wildfire is altering the terrestrial carbon balance. However, the degree to which novel wildfire regimes disrupt biological function remains unclear. Here, we synthesize the current understanding of above and below ground processes that govern carbon loss and recovery across diverse ecosystems. In this study, we find that intensifying wildfire regimes are increasingly exceeding biological thresholds of resilience, causing ecosystems to convert to a lower carbon-carrying capacity. Growing evidence suggests that plants compensate for fire damage by allocating carbon below ground to access nutrients released by fire, while wildfire selects for microbial communities with rapid growth rates and the ability to metabolize pyrolyzed carbon. Determining controls on carbon dynamics following wildfire requires integration of experimental and modeling frameworks across scales and ecosystems. Fire severity is expected to increase as a result of warming. This will potentially amplify climate change due to its impact on the carbon cycle. This Review discusses ecosystem carbon loss and recovery following wildfire, and highlights where further work is needed to inform model predictions.

54 ENVIRONMENTAL SCIENCES↗

Carbon-phosphorus cycle models overestimate CO 2 enrichment response in a mature Eucalyptus forest

The importance of phosphorus (P) in regulating ecosystem responses to climate change has fostered P-cycle implementation in land surface models, but their CO 2 effects predictions have not been evaluated against measurements. Here, we perform a data-driven model evaluation where simulations of eight widely used P-enabled models were confronted with observations from a long-term free-air CO 2 enrichment experiment in a mature, P-limited Eucalyptus forest. We show that most models predicted the correct sign and magnitude of the CO 2 effect on ecosystem carbon (C) sequestration, but they generally overestimated the effects on plant C uptake and growth. We identify leaf-to-canopy scaling of photosynthesis, plant tissue stoichiometry, plant belowground C allocation, and the subsequent consequences for plant-microbial interaction as key areas in which models of ecosystem C-P interaction can be improved. Together, this data-model intercomparison reveals data-driven insights into the performance and functionality of P-enabled models and adds to the existing evidence that the global CO 2 -driven carbon sink is overestimated by models.

54 ENVIRONMENTAL SCIENCES↗

Climate adaptation and sustainability in switchgrass: exploring plant-microbe-soil interactions across continental scale environmental gradients

Less carbon-intensive energy sources are needed to reduce greenhouse gas emissions and their predicted role in climate change. There is growing interest in the potential of biofuels for meeting this need. A critical question is whether large-scale biofuel production can be sustainable over the time scales needed to mitigate our carbon debt from fossil fuel consumption. The carbon balance and ultimately the sustainability of biofuel feedstock production is the result of complex climate-coupled interactions between carbon fixation, sequestration, and release through combustion. Similarly, the long-term productivity of biofuels depends on the environmental factors limiting plant growth. These factors are often related to soil resources which involve complex interactions at the plant-microbe-soil interface impacting their availability and cycling. Our collaborative project addressed sustainable switchgrass (Panicum virgatum) production by exploring Plant Systems, Plant-Microbiome Interactions, and Ecosystem Processes through the integrating lens of Multi-Scale Modeling. Our research was based on detailed characterization of genetically diverse switchgrass genotypes planted in common gardens across a continental latitudinal gradient. The underlying theme of our Plant Systems research was the use of locally adapted plant material to explore plant function, to understand the mechanistic basis of environmental interactions, and to discover the plant genes important for adaptation and sustainability in the face of climate change. Our Plant-Microbiome Interaction project characterized the microbial communities associated with switchgrass using genomic tools. Our Ecosystem Processes research focused on carbon cycle responses at the ecosystem level using stand level plantings. Finally, our Multi-Scale Modeling helped to define conditions of a sustainable biofuel system and identify key tradeoffs between genetic diversity, productivity, and ecosystem services. Genome-wide association analyses were used to identify alleles that contribute to successful establishment and biomass production across North America. Together, our work provided a baseline analyses of the potential of switchgrass as a biofuel feedstock. Our project resulted in a number of successful outcomes. First, we were successful in collecting switchgrass germplasm across the species range, propagating the material, and establishing common garden experiments across the species range. In collaboration with DOE JGI, we successfully assembled the first tetraploid switchgrass genome and published this resource with an analyses of the genetic basis local adaptation from our gardens (Lowry et al. 2019, Lovell et al. 2021). The gardens were used to characterize the genetic architecture for a number of important plant phenotypes. Our project also conducted extensive sampling and sequencing to characterize the bacterial and fungal associates of switchgrass roots and leaves. We showed that host genotype, location, and harvesting practices can play a role in microbiome assembly (Singer et al. 2019 & 2022, Van Wallendael et al. 2020 & 2022, Edwards et al. 2023). Our ecosystem processes work created baseline dataset of carbon and nutrient cycling in realistic stand plantings of switchgrass. Data from this experiment provided new insight into the role of plant traits, phenology, and local environments in ecosystem processes like soil respiration, net-ecosystem exchange, and dynamics of soil and plant nutrients (Ricketts et al. 2023). Finally, our crop modelling experiments help to characterize the sensitivity of common modeling frameworks to parameters, identify key limiters of productivity across large geographic scales, and leverage patterns of local adaptation in prediction. Ultimately, these studies help to identify critical plant-microbe-soil traits that may be manipulated, through breeding or agronomic management, to improve the sustainability of biofuel feedstocks.

09 BIOMASS FUELS↗

Soil microbiome feedbacks during disturbance-driven forest ecosystem conversion

Abstract Disturbances cause rapid changes to forests, with different disturbance types and severities creating unique ecosystem trajectories that can impact the underlying soil microbiome. Pile burning—the combustion of logging residue on the forest floor—is a common fuel reduction practice that can have impacts on forest soils analogous to those following high-severity wildfire. Further, pile burning following clear-cut harvesting can create persistent openings dominated by nonwoody plants surrounded by dense regenerating conifer forest. A paired 60-year chronosequence of burn scar openings and surrounding regenerating forest after clear-cut harvesting provides a unique opportunity to assess whether belowground microbial processes mirror aboveground vegetation during disturbance-induced ecosystem shifts. Soil ectomycorrhizal fungal diversity was reduced the first decade after pile burning, which could explain poor tree seedling establishment and subsequent persistence of herbaceous species within the openings. Fine-scale changes in the soil microbiome mirrored aboveground shifts in vegetation, with short-term changes to microbial carbon cycling functions resembling a postfire microbiome (e.g. enrichment of aromatic degradation genes) and respiration in burn scars decoupled from substrate quantity and quality. Broadly, however, soil microbiome composition and function within burn scar soils converged with that of the surrounding regenerating forest six decades after the disturbances, indicating potential microbial resilience that was disconnected from aboveground vegetation shifts. This work begins to unravel the belowground microbial processes that underlie disturbance-induced ecosystem changes, which are increasing in frequency tied to climate change.

Nelson, Amelia R.↗

Microbial community dynamics and coexistence in a sulfide-driven phototrophic bloom

Background: Lagoons are common along coastlines worldwide and are important for biogeochemical element cycling, coastal biodiversity, coastal erosion protection and blue carbon sequestration. These ecosystems are frequently disturbed by weather, tides, and human activities. Here, we investigated a shallow lagoon in New England. The brackish ecosystem releases hydrogen sulfide particularly upon physical disturbance, causing blooms of anoxygenic sulfur-oxidizing phototrophs. To study the habitat, microbial community structure, assembly and function we carried out in situ experiments investigating the bloom dynamics over time. Results: Phototrophic microbial mats and permanently or seasonally stratified water columns commonly contain multiple phototrophic lineages that coexist based on their light, oxygen and nutrient preferences. We describe similar coexistence patterns and ecological niches in estuarine planktonic blooms of phototrophs. The water column showed steep gradients of oxygen, pH, sulfate, sulfide, and salinity. The upper part of the bloom was dominated by aerobic phototrophic Cyanobacteria, the middle and lower parts by anoxygenic purple sulfur bacteria (Chromatiales) and green sulfur bacteria (Chlorobiales), respectively. We show stable coexistence of phototrophic lineages from five bacterial phyla and present metagenome-assembled genomes (MAGs) of two uncultured Chlorobaculum and Prosthecochloris species. In addition to genes involved in sulfur oxidation and photopigment biosynthesis the MAGs contained complete operons encoding for terminal oxidases. The metagenomes also contained numerous contigs affiliating with Microviridae viruses, potentially affecting Chlorobi. Our data suggest a short sulfur cycle within the bloom in which elemental sulfur produced by sulfide-oxidizing phototrophs is most likely reduced back to sulfide by Desulfuromonas sp. Conclusions: The release of sulfide creates a habitat selecting for anoxygenic sulfur-oxidizing phototrophs, which in turn create a niche for sulfur reducers. Strong syntrophism between these guilds apparently drives a short sulfur cycle that may explain the rapid development of the bloom. The fast growth and high biomass yield of Chlorobi-affiliated organisms implies that the studied lineages of green sulfur bacteria can thrive in hypoxic habitats. This oxygen tolerance is corroborated by oxidases found in MAGs of uncultured Chlorobi. The findings improve our understanding of the ecology and ecophysiology of anoxygenic phototrophs and their impact on the coupled biogeochemical cycles of sulfur and carbon.

59 BASIC BIOLOGICAL SCIENCES↗

Ecosystem Water‐Saving Timescale Varies Spatially With Typical Drydown Length

Abstract Stomatal optimization theory is a commonly used framework for modeling how plants regulate transpiration in response to the environment. Most stomatal optimization models assume that plants instantaneously optimize a reward function such as carbon gain. However, plants are expected to optimize over longer timescales given the rapid environmental variability they encounter. There are currently no observational constraints on these timescales. Here, a new stomatal model is developed and is used to analyze the timescales over which stomatal closure is optimized. The proposed model assumes plants maximize carbon gain subject to the constraint that they cannot draw down soil moisture below a critical value. The reward is integrated over time, after being weighted by a discount factor that represents the timescale ( τ ) that a plant considers when optimizing stomatal conductance to save water. The model is simple enough to be analytically solvable, which allows the value of τ to be inferred from observations of stomatal behavior under known environmental conditions. The model is fitted to eddy covariance data in a range of ecosystems, finding the value of τ that best predicts the dynamics of evapotranspiration at each site. Across 82 sites, the climate metrics with the strongest correlation to τ are measures of the average number of dry days between rainfall events. Values of τ are similar in magnitude to the longest such dry period encountered in an average year. The results here shed light on which climate characteristics shape spatial variations in ecosystem‐level water use strategy.

54 ENVIRONMENTAL SCIENCES↗

Fusion of Multiple Models for Improving Gross Primary Production Estimation With Eddy Covariance Data Based on Machine Learning

Abstract Terrestrial gross primary production (GPP) represents the magnitude of CO 2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight ecosystems based on FLUXNET2015 data set using the ensemble methods of Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF) separately. Our results showed that for individual models, EC‐LUE gave a better performance to capture interannual variability of GPP than other models, followed by VPM and GLO‐PEM, while CHJ and C‐Fix were more limited in their estimation performance. We found RF and SVM were superior to BMA on merging individual models at various plant functional types (PFTs) and at the scale of individual sites. On the basis of individual models, the fusion methods of BMA, SVM, and RF were examined by a five‐fold cross validation for each ecosystem type, and each method successfully improved the average accuracy of estimation by 8%, 18%, and 19%, respectively.

Environmental Sciences & Ecology↗

Metagenomes in the Borderline Ecosystems of the Antarctic Cryptoendolithic Communities

Antarctic cryptoendolithic communities are microbial ecosystems dwelling inside rocks of the Antarctic desert. We present the first 18 shotgun metagenomes from these communities to further characterize their composition, biodiversity, functionality, and adaptation. Future studies will integrate taxonomic and functional annotations to examine the pathways necessary for life to evolve in the extremes.

59 BASIC BIOLOGICAL SCIENCES↗

Recommendations for Minimum Required Error Codes for Electric Vehicle Charging Infrastructure

OCPP protocol manages the interaction between the EVSE and its respective back-end communication network. It plays a pivotal role in both error reporting and troubleshooting, carried out primarily through the CSMS. OCPP defines both standard error codes and a flexible framework for creating and communicating custom error codes. The OCPI protocol orchestrates the communication between different backhaul communication networks, incorporating the exchange of error codes. These error codes are instrumental in pinpointing and rectifying issues that can surface prior to, during, or after charging operations, fortifying the reliability and resilience of the EV charging infrastructure. The flexibility offered by the OCPP and OCPI frameworks through the introduction of custom error codes also creates its own set of challenges. While the integration of custom error codes allows for enhanced granularity, it also introduces inconsistencies and fragmentation within the overarching diagnostic reporting system. To address the challenges with custom error codes this report proposes a set of Minimum Required Error Codes (MRECs) for streamlined error reporting, interpretability, and diagnostics. Recommendations in this report are based on independent analysis of custom error codes from multiple stakeholders within the EV charging ecosystem. For better error resolution, this report also assigns one or more entities responsible for the resolution of every mentioned error code. Finally, a functional classification for each mentioned error code is also identified to describe the nature of the error. In summary, the purpose of this document is to simplify the troubleshooting process and increase charging reliability for all EV users. This report serves as a recommendation for industry stakeholders, encouraging a unified methodology to define, transmit, and interpret common error codes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The SpacePy space science package at 12 years

For over a decade, the SpacePy project has contributed open-source solutions for the production and analysis of heliophysics data and simulation results. Here we introduce SpacePy’s functionality for the scientific user and present relevant design principles. We examine recent advances and the future of SpacePy in the broader scientific Python ecosystem, concluding with some of the work that has used SpacePy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Microbial contribution to post-fire tundra ecosystem recovery over the 21st century

Abstract Tundra ecosystems have experienced an increased frequency of fire, and this trend is predicted to continue throughout the 21 st Century. Post-fire recovery is underpinned by complex interactions between microbial functional groups that drive nutrient cycling. Here we use a mechanistic model to demonstrate an acceleration of the nitrogen cycle post-fire driven by changes in niche space and microbial competitive dynamics. We show that over the first 5-years post-fire, fast-growing bacterial heterotrophs colonize regions of the soil previously occupied by slower-growing saprotrophic fungi. The bacterial heterotrophs mineralize organic matter, releasing nutrients into the soil. This pathway outweighs new sources of nitrogen and facilitates the recovery of plant productivity. We broadly show here that while consideration of distinct microbial metabolisms related to carbon and nutrient cycling remains rare in terrestrial ecosystem models, they are important when considering the rate of ecosystem recovery post-disturbance and the feedback to soil nutrient cycles on centennial timescales.

54 ENVIRONMENTAL SCIENCES↗