Amplifying effects of recurrent drought on the dynamics of tree growth and water use in a subalpine forest

Despite recent advances in our understanding of drought impacts on tree functioning, we lack knowledge about the dynamic responses of mature trees to recurrent drought stress. At a subalpine forest site, we assessed the effects of three years of recurrent experimental summer drought on tree growth and water relations of Larix decidua Mill. and Picea abies (L. Karst.), two common European conifers representative for contrasting water-use strategies. We combined dendrometer and xylem sap flow measurements with analyses of xylem anatomy and non-structural carbohydrates and their carbon-isotope composition. Recurrent drought increased the effects of soil moisture limitation on growth and xylogenesis, and to a lesser extent on xylem sap flow. P. abies showed stronger growth responses to recurrent drought, reduced starch concentrations in branches and increased water-use efficiency when compared to L. decidua. Despite comparatively larger maximum tree water deficits than in P. abies, xylem formation of L. decidua was less affected by drought, suggesting a stronger capacity of rehydration or lower cambial turgor thresholds for growth. Our study shows that recurrent drought progressively increases impacts on mature trees of both species, which suggests that in a future climate increasing drought frequency could impose strong legacies on carbon and water dynamics of treeline species

CROMES - A fast and efficient machine learning emulator pipeline for gridded crop models

Global gridded crop models (GGCMs) have become state-of-the-art tools in large-scale climate impact and adaptation assessments. Yet, these combinations of large-scale spatial data frameworks and plant growth models have limitations in the volume of scenarios they can address due to computational demand and complex software structures. Emulators mimicking such models have therefore become an attractive option to produce reasonable predictions of GGCMs’ crop productivity estimates at much lower computational costs. However, such emulators’ flexibility is thus far typically limited in terms of crop management flexibility and spatial resolutions among others. Here we present a new emulator pipeline CROp model Machine learning Emulator Suite (CROMES) that serves for processing climate features from netCDF input files, combining these with site-specific features (soil, topography), and crop management specifications (planting dates, cultivars, irrigation) to train machine learning emulators and subsequently produce predictions. Presently built around the GGCM EPIC-IIASA and employing a boosting algorithm, CROMES is capable of producing predictions for EPIC-IIASA’s crop yield estimates with high accuracy and very high computational efficiency. Predictions require for a first used climate dataset about 45 min and 10 min for any subsequent scenario based on the same climate forcing in a single thread compared to approx. 14h for a GGCM simulation on the same system. Prediction accuracy is highest if modeling the case when crops receive sufficient nutrients and are consequently most sensitive to climate. When training an emulator on crop model simulations for rainfed maize and a single global climate model (GCM), the yield prediction accuracy for out-of-bag GCMs is R2=0.93-0.97, RMSE=0.5-0.7, and rRMSE=8-10% in space and time. Globally, the best agreement between predictions and crop model simulations occurs in (sub-)tropical regions, the poorest is in cold, arid climates where both growing season length and water availability limit crop growth. The performance slightly deteriorates if fertilizer supply is considered, more so at low levels of nutrient inputs than at the higher end. Importantly, emulators produced by CROMES are virtually scale-free as all training samples, i.e., pixels, are pooled and hence treated as individual locations solely based on features provided without geo-referencing. This allows for applications on increasingly available high-resolution climate datasets or in regional studies for which more granular data may be available than at global scales. Using climate features based on crop growing seasons and cardinal growth stages enables also adaptation studies including growing season and cultivar shifts. We expect CROMES to facilitate explorations of comprehensive climate projection ensembles, studies of dynamic climate adaptation scenarios, and cross-scale impact and adaptation assessments

CROMES v1.0: a flexible CROp Model Emulator Suite for climate impact assessment

Global gridded crop models (GGCMs) are simulation tools designed for global, spatially explicit estimation of crop productivity and associated externalities. Key areas for their application are climate impact and adaptation studies. As GGCMs are typically computationally costly and require comprehensive data pre- and post-processing, GGCM emulators are gaining increasing popularity. Earlier emulators have typically been published pre-trained on synthetic weather and management combinations. Here, we present a novel computational pipeline CROp Model Emulator Suite (CROMES) v1.0 that serves for flexibly training GGCM emulators on data commonly available from GGCM simulations. Essentially, CROMES consists of modules to (1) process climate data from daily resolution netCDF files to (sub-)growing season aggregates as climate features, (2) combine various feature types (climate, soil, crop management), (3) train emulators using machine-learning algorithms, and (4) produce predictions. Exemplary, we apply CROMES to train emulators on simulations for rainfed maize from the GGCM EPIC-IIASA and climate projections from a single GCM to subsequently test their skill in predicting crop yields for unseen climate projections from other GCMs. Depending on the training and target data, the regression statistics between GGCM simulations and predictions across all points in time and space are in the ranges R2 = 0.97 to 0.98, slope = 0.99 to 1.01, and intercept = −0.06 to +0.06. The RMSE ranges between 0.49 and 0.65 t ha−1. Spatially, patterns are evident with lowest performance in (semi-)arid regions where aggregation of weather data may result in higher information loss while permanent crop growth limitations may hamper evaluation statistics as well. The gain in computational speed for predictions is at more than an order of magnitude with time required to produce target features and subsequent predictions at about 30min on common hardware. We expect CROMES to be of utility in covering more comprehensively uncertainty in climate impact projections, evaluations of adaptation options, and spatio-temporal assessments of crop productivity

Excessive folate synthesis limits lifespan in the C. elegans: E. coli aging model

Background: Gut microbes influence animal health and thus, are potential targets for interventions that slow aging. Live E. coli provides the nematode worm Caenorhabditis elegans with vital micronutrients, such as folates that cannot be synthesized by animals. However, the microbe also limits C. elegans lifespan. Understanding these interactions may shed light on how intestinal microbes influence mammalian aging. Results: Serendipitously, we isolated an E. coli mutant that slows C. elegans aging. We identified the disrupted gene to be aroD, which is required to synthesize aromatic compounds in the microbe. Adding back aromatic compounds to the media revealed that the increased C. elegans lifespan was caused by decreased availability of para-aminobenzoic acid, a precursor to folate. Consistent with this result, inhibition of folate synthesis by sulfamethoxazole, a sulfonamide, led to a dose-dependent increase in C. elegans lifespan. As expected, these treatments caused a decrease in bacterial and worm folate levels, as measured by mass spectrometry of intact folates. The folate cycle is essential for cellular biosynthesis. However, bacterial proliferation and C. elegans growth and reproduction were unaffected under the conditions that increased lifespan. Conclusions: In this animal:microbe system, folates are in excess of that required for biosynthesis. This study suggests that microbial folate synthesis is a pharmacologically accessible target to slow animal aging without detrimental effects

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Strong floristic distinctiveness across Neotropical successional forests.

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (<20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained

Strong floristic distinctiveness across Neotropical successional forests

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (≤20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained

AI4SoilHealth: Supporting Europe’s Soil Deal using AI Technology and Predictive Services

Prompted by the European Commission’s goal to improve soil health by 2030, also known as “Mission Soil”, the AI4SoilHealth project directly aims to implement and improve soil health monitoring and modelling services by leveraging AI technology. Central to the initiative is the development of a Soil Digital Twin, which will offer real-time forecasting and assessment of soil health indicators. These predictive tools will be integrated into practical applications, such as a Soil Health Index and an AI-powered app, enabling farmers and policymakers to make informed decisions. By improving soil health management through these services, the project supports the EU's goal of healthier soils by 2030. IIASA, as a facilitator of policy solutions to environmental challenges, supports this initiative by providing efficient methods to predict how soil health changes in the future as a function of climate change and management decisions. Biophysical crop models are vital for answering such question, as purely data- driven approaches lack the extrapolation capabilities for predictions far into the future. However, their complexity often leads to significant computational demands and the need for location-specific parameters. To address these challenges, we propose a data-driven approach that emulates crop model simulations using the EPIC-IIASA model. The emulator, based on third-order polynomial regression models, is trained on diverse climate, soil, and management data. It predicts average crop yield and carbon sequestration potentials by breaking down the problem into an ensemble of smaller models, allowing for efficient, large-scale predictions into the future with well- defined uncertainty intervals and explainable results. The approach is demonstrated through two use cases: a Farmer’s Advice app that informs users on how management practices affect yield now and up to the year 2100, and a broader application across 86,000 units in Europe. By emulating crop models with data-driven techniques, we can drastically reduce computational effort while opening up new applications, including citizen science and integration into larger model ensembles