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In human and other mammalian cells, transport of L -lactate across plasma membranes is mainly catalyzed by monocarboxylate transporters (MCTs) of the SLC16 solute carrier family. MCTs play an important role in cancer metabolism and are promising targets for tumor treatment. Here, we report the crystal structures of an SLC16 family homologue with two different bound ligands at 2.54 and 2.69 Å resolution. The structures show the transporter in the pharmacologically relevant outward-open conformation. Structural information together with a detailed structure-based analysis of the transport function provide important insights into the molecular working mechanisms of ligand binding and L -lactate transport. The transport of L -lactate across plasma membranes is catalyzed by proton-driven monocarboxylate transporters (MCTs) of the SLC16 solute carrier family. Here, the authors present the crystal structures of a bacterial SLC16 homologue with the bound substrate L -lactate and ligand thiosalicylate both in an outward-open conformation and discuss the L -lactate transport mechanism.

The hierarchy of channel networks in landscapes displays features that are characteristic of nonequilibrium complex systems. Here we show that a sequence of increasingly complex ridge and valley networks is produced by a system of partial differential equations coupling landscape evolution dynamics with a specific catchment area equation. By means of a linear stability analysis we identify the critical conditions triggering channel formation and the emergence of characteristic valley spacing. The ensuing channelization cascade, described by a dimensionless number accounting for diffusive soil creep, runoff erosion, and tectonic uplift, is reminiscent of the subsequent instabilities in fluid turbulence, while the structure of the simulated patterns is indicative of a tendency to evolve toward optimal configurations, with anomalies similar to dislocation defects observed in pattern-forming systems. The choice of specific geomorphic transport laws and boundary conditions strongly influences the channelization cascade, underlying the nonlocal and nonlinear character of its dynamics.

The topography of a landscape regulates the spatial distribution of water and energy fluxes, which are main drivers of vegetation and soil carbon and nutrient dynamics. Despite the recognized role of topography in mediating such processes, quantifying and predicting the spatial distribution of carbon and nutrient fluxes and stocks in highly heterogeneous landscapes remains challenging. The main limitations stem from the prevalence of largely decoupled modeling approaches which fail to concurrently account for ecohydrological and biogeochemical processes as well as the lack of adequate frameworks describing the links among topography, water and energy balances, and soil biogeochemical dynamics. Here, we extend the capabilities of the mechanistic ecohydrological model Tethys‐Chloris‐Biogeochemistry (T&C‐BG) by including a soil carbon and nutrient routing module in the distributed model version. The newly developed T&C‐BG‐2D model is validated against long‐term hydrological and biogeochemical measurements from the Hafren catchment in Wales (UK) and the Erlenbach catchment in the Swiss pre‐Alps. The model successfully captures carbon and nutrient concentrations and dynamics in these catchments, with relative differences between simulated and observed median values of between −4% and −0.3% for dissolved organic carbon, and between 1% and 20% for ammonia. A sensitivity analysis in the Erlenbach basin suggests that elevation explains over 80% of the observed spatial patterns, followed by topographic wetness index (12.6%), aspect (2.9%), and curvature (2.1%). These findings underscore topography's critical role in shaping water, carbon, and nutrient dynamics, which cannot be reflected in plot‐scale simulations neglecting spatial interactions and topographic effects.

The SIR ('susceptible-infectious-recovered') formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number Ro inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled Ro = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of Ro = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of 'super-spreaders' on the inference of Ro are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled Ro are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in Ro > 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.

Saturated hydraulic conductivity (Ksat) is a key soil hydraulic parameter for representing infiltration and drainage in land surface models. For large scale applications, Ksat is often estimated from pedotransfer functions (PTFs) based on easy‐to‐measure soil properties like soil texture and bulk density. The reliance of PTFs on data from uniform arable lands and the omission of soil structure limits the applicability of texture‐based predictions of Ksat in vegetated lands. To include effects of terrain, climate, and vegetation in the derivation of a new global Ksat map at 1 km resolution, we harness technological advances in machine learning and availability of remotely sensed surrogate information. For model training and testing, a global compilation of 6,814 geo‐referenced Ksat measurements from the literature was used. The accuracy assessment based on spatial cross‐validation shows a concordance correlation coefficient (CCC) of 0.16 and a root mean square error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC = 0.79 and RMSE = 0.72 for non‐spatial cross‐validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on easy‐to‐measure soil properties. The validation of the model indicates that Ksat could be modeled without bias using Covariate‐based GeoTransfer Functions (CoGTFs) that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data. Plain Language Summary The soil saturated hydraulic conductivity (Ksat) defines how fast water infiltrates into and percolates through the soil. To model water flow at large scales, accurate maps of Ksat are needed. Usually, Ksat is not measured directly but deduced from well‐known basic soil properties (e.g., soil texture, bulk density). However, these estimates neglect the influence of vegetation and climate on formation of soil structures that control Ksat. To improve global predictions of Ksat, we use a new spatially referenced Ksat data collection and apply machine learning to exploit correlations between Ksat and other properties (e.g., soil information, terrain, climate, and vegetation). These correlations are then implemented at global scale using maps of all relevant properties (so called “environmental covariates”) that were measured by remote sensing. We call this new approach to predictive Ksat mapping “Covariate‐based GeoTransfer Function” (CoGTF) to highlight differences with other maps that neglect spatial correlation with soil formation processes and that are based only on soil data (so called “pedotransfer functions”, PTFs). We show that the new maps based on CoGTF perform better than approaches based on PTFs. Key Points Climate, vegetation, and terrain affect spatial patterns of saturated hydraulic conductivity (Ksat) The effect of these environmental covariates on Ksat is quantified using remote sensing data and machine learning We introduce Covariate‐based GeoTransfer Functions to improve Ksat predictions based on pedotransfer functions

Evidence suggests that the response of rainfed crops to dry or wet years is modulated by soil texture. This is a central tenet for certain agronomic operations in water-limited regions that rely on spatial distribution of soil texture for guiding precision agriculture. In contrast, natural vegetation in climatic equilibrium evolves to form a dynamic assemblage of traits and species adapted to local climatic conditions, primarily precipitation in water-limited regions. For undisturbed landscapes, we hypothesize that natural vegetation responds to rainfall anomalies irrespectively of local soil texture whereas rainfed crops are expected to respond to texture-mediated plant available water. Earth system models (ESMs) often quantify vegetation response to drought and water stress based on traditional agronomic concepts despite fundamental differences in composition and traits of natural vegetation and crops. We seek to test the hypothesis above at local and regional scales to differentiate natural vegetation and rainfed crops response to rainfall anomalies across soil types and better link them to water and carbon cycles. We employed field observations and remote sensing data to systematically examine the response of natural and rainfed cropped vegetation across biomes and scales. At local scales (field to ∼0.1 km), we used crop yields from literature data and natural vegetation productivity as gross primary productivity (GPP) from adjacent FLUXNET sites. At regional scales (∼10 2 km), we rely exclusively on remote-sensing-based GPP. Results confirm a lack of response of natural vegetation productivity to soil texture across biomes and rainfall anomalies at all scales. In contrast, crop yields at field scale exhibit correlation with soil texture in dry years (in agreement with conventional agronomic practices). These results support the hypothesis that natural vegetation is decoupled from soil texture, whereas rainfed crops retain dependency on soil texture in dry years. However, the observed correlation of crops with soil texture becomes obscured at larger scales by spatial variation of topography, rainfall, and uncertainty in soil texture and GPP values. The study provides new insights into what natural vegetation’s climatic equilibrium might mean and reveals the role of scale in expressing such sensitivities in ESMs.

Agricultural production in arid and semi-arid regions is particularly vulnerable to climate change, which, combined with projected food requirements, makes the sustainable management of water resources critical to ensure national and global food security. Using South Africa as an example, we map the spatial distribution of water use by seventeen major crops under current and future climate scenarios, and assess their sustainability in terms of water resources, using the water debt repayment time indicator. We find high water debts, indicating unsustainable production, for potatoes, pulses, grapes, cotton, rice, and wheat due to irrigation in arid areas. Climate change scenarios suggest an intensification of such pressure on water resources, especially in regions already vulnerable, with a country-scale increase in irrigation demand of between 6.5% and 32% by 2090. Future land use planning and management should carefully consider the spatial distribution and local sustainability of crop water requirements to reduce water consumption in water risk hotspots and guarantee long-term food security.

Earth system models use soil information to parameterize hard-to-measure soil hydraulic properties based on pedotransfer functions. However, current parameterizations rely on sample-scale information which often does not account for biologically-promoted soil structure and heterogeneities in natural landscapes, which may significantly alter infiltration-runoff and other exchange processes at larger scales. Here we propose a systematic framework to incorporate soil structure corrections into pedotransfer functions, informed by remote-sensing vegetation metrics and local soil texture, and use numerical simulations to investigate their effects on spatially distributed and areal averaged infiltration-runoff partitioning. We demonstrate that small scale soil structure features prominently alter the hydrologic response emerging at larger scales and that upscaled parameterizations must consider spatial correlations between vegetation and soil texture. The proposed framework allows the incorporation of hydrological effects of soil structure with appropriate scale considerations into contemporary pedotransfer functions used for land surface parameterization.

Hydrological and climatic modeling of near-surface water and energy fluxes is critically dependent on the availability of soil hydraulic parameters. Key among these parameters is the soil water characteristic curve (SWCC), a function relating soil water content (θ) to matric potential (ψ). The direct measurement of SWCC is laborious, hence, reported values of SWCC are spatially sparse and usually have only a small number of data pairs (θ, ψ) per sample. Pedotransfer function (PTF) models have been used to correlate SWCC with basic soil properties, but evidence suggests that SWCC is also shaped by vegetation-promoted soil structure and climate-modified clay minerals. To capture these effects in their spatial context, a machine learning framework (denoted as Covariate-based GeoTransfer Functions, CoGTFs) was trained using (a) a novel and comprehensive global dataset of SWCC parameters and (b) global maps of environmental covariates and soil properties at 1 km spatial resolution. Two CoGTF models were developed: one model (CoGTF-1) was based on predicted soil covariates because measured soil data are not generally available, and the other (CoGTF-2) used measured soil properties to model SWCC parameters. The spatial cross-validation of CoGTF-1 resulted, for the predicted van Genuchten SWCC parameters, in concordance correlation coefficients (CCC) of 0.321–0.565. To validate the resulting global maps of SWCC parameters and to compare the CoGTF framework to two pedotransfer functions from the literature, the predicted water contents at 0.1 m, 3.3 m, and 150 m matric potential were evaluated. The accuracy metrics for CoGTF were considerably better than PTF-based maps.

Agrivoltaic systems are characterized by the co‐existence of photovoltaic panels on agricultural land, allowing simultaneous solar energy and food production without need for further land. Agrivoltaic installations alter the local microclimatic conditions of the land surface, impacting the performance of the agricultural systems embedded in them. In this study we develop an ecohydrological modeling framework combining a module that simulates changes in micrometeorology due to photovoltaic panel installations with a state‐of‐the‐art model that resolves land surface water, energy, and vegetation dynamics (i.e., the terrestrial biosphere model T&C). We demonstrate that the modeling framework is capable of reproducing grassland dynamics across a broad range of climates and agrivoltaic architectures. With the use of the model we evaluated grassland performance across the Mediterranean for two most commonly used architectures, namely mixed mounted solar panels and rotating solar tracking panels. We found that C3 grassland yields can be significantly enhanced only in climates where annual potential evapotranspiration exceeds annual rainfall. Changes in grassland productivity were attributed primarily to changes in the light environment at the land surface, with changes in surface aerodynamic roughness and rainfall redistribution due to drainage on panels playing a smaller negative role of comparable magnitudes. Plain Language Summary Installing photovoltaic panels on agricultural land has the potential to boost sustainable electricity production, whilst minimizing evaporation crop water losses. Such installations are termed agrivoltaics. Assessing how crops perform within an agrivoltaic installation is challenging due to the complex interactions of sun shading, precipitation redistribution due to drainage on the photovoltaic panels and the change of near surface airflows. In this study we develop a model that can predict crop dynamics under the complex micrometeorological changes agrivoltaic installations cause. Using the model we show that agrivoltaics are particularly efficient in boosting crop yields in semi‐arid areas, however they can lead to crop yield reductions in wetter areas. The dominant micrometeorological factor leading to crop yield enhancement is the shading provided by the photovoltaic panels. Key Points We integrated agrivoltaic installations in an earth system model Agrivoiltaics increase grassland productivity when PET exceeds precipitation Shading is the most important micrometeorological factor explaining grassland ecohydrological dynamics