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• A valid representation of intra-annual wood formation processes in global vegetation models is vital for assessing climate change impacts on the forest carbon stock. Yet, wood formation is generally modelled with photosynthesis, despite mounting evidence that cambial activity is rather directly constrained by limiting environmental factors. • Here, we apply a state-of-the-art turgor-driven growth model to simulate 4 yr of hourly stem radial increment from Picea abies (L.) Karst. and Larix decidua Mill. growing along an elevational gradient. For the first time, wood formation observations were used to validate weekly to annual stem radial increment simulations, while environmental measurements were used to assess the climatic constraints on turgor-driven growth. • Model simulations matched the observed timing and dynamics of wood formation. Using the detailed model outputs, we identified a strict environmental regulation on stem growth (air temperature > 2°C and soil water potential > −0.6 MPa). Warmer and drier summers reduced the growth rate as a result of turgor limitation despite warmer temperatures being favourable for cambial activity. • These findings suggest that turgor is a central driver of the forest carbon sink and should be considered in next-generation vegetation models, particularly in the context of global warming and increasing frequency of droughts.

Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative—process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process—correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.

Exceptions to the generality of the stress-gradient hypothesis (SGH) may be reconciled by considering species-specific traits and stress tolerance strategies. Studies have tested stress tolerance and competitive ability in mediating interaction outcomes, but few have incorporated this to predict how species interactions shift between competition and facilitation along stress gradients. We used field surveys, salt tolerance and competition experiments to develop a predictive model interspecific interaction shifts across salinity stress gradients. Field survey and greenhouse tolerance tests revealed tradeoffs between stress tolerance and competitive ability. Modeling showed that along salinity gradients, (1) plant interactions shifted from competition to facilitation at high salinities within the physiological limits of salt-intolerant plants, (2) facilitation collapsed when salinity stress exceeded the physiological tolerance of salt-intolerant plants, and (3) neighbor removal experiments overestimate interspecific facilitation by including intraspecific effects. A community-level field experiment, suggested that (1) species interactions are competitive in benign and, facilitative in harsh condition, but fuzzy under medium environmental stress due to niche differences of species and weak stress amelioration, and (2) the SGH works on strong but not weak stress gradients, so SGH confusion arises when it is applied across questionable stress gradients. Our study clarifies how species interactions vary along stress gradients. Moving forward, focusing on SGH applications rather than exceptions on weak or nonexistent gradients would be most productive.

Applications of process‐based models (PBM) for predictions are confounded by multiple uncertainties and computational burdens, resulting in appreciable errors. A novel modeling framework combining a high‐fidelity PBM with surrogate and machine learning (ML) models is developed to tackle these challenges and applied for streamflow prediction. A surrogate model permits high computational efficiency of a PBM solution at a minimum loss of its accuracy. A novel probabilistic ML model partitions the PBM‐surrogate prediction errors into reducible and irreducible types, quantifying their distributions that arise due to both explicitly perceived uncertainties (such as parametric) or those that are entirely hidden to the modeler (not included or unexpected). Using this approach, we demonstrate a substantial improvement of streamflow predictive accuracy for a case study urbanized watershed. Such a framework provides an efficient solution combining the strengths of high‐fidelity and physics‐agnostic models for a wide range of prediction problems in geosciences. Plain Language Summary This study proposes a new framework that combines three different modeling techniques to make flood forecasting more accurate. The framework combines the strengths of (a) complex models (or process‐based models, PBMs) based on our understanding of relevant processes that can reproduce measurable quantities; (b) simpler models that are designed to mimic PBM's solutions—known as surrogate models—and make predictions within a few seconds; and (c) machine learning models that can detect relationships among variables using only data, improve the accuracy of prediction, and provide estimates of prediction uncertainty. The framework is tested in an urbanized watershed and shows a significant improvement in both computational efficiency and accuracy of streamflow prediction. Ultimately, the proposed framework is a novel powerful solution that combines the latest advances in different types of modeling approaches to solve prediction problems in geosciences. Its adaptability and efficiency make it suitable for a wide range of situations. Key Points While PBMs are physics‐based, the complexity of uncertainties and the high computational burden have limited their utility for predictions The developed novel framework integrates process‐based models, surrogate, and machine learning (ML) models to predict ensemble flood attributes with error quantification A novel probabilistic ML model partitions the errors into reducible and irreducible types, also quantifying their distributions

Summary Climate models predict that the frequency, intensity and duration of drought events will increase in tropical regions. Although water withdrawal from deep soil layers is generally considered to be an efficient adaptation to drought, there is little information on the role played by deep roots in tropical forests. Tropical Eucalyptus plantations managed in short rotation cycles are simple forest ecosystems that may provide an insight into the water use by trees in tropical forests. The contribution made by water withdrawn from deep soil layers to the water required for evapotranspiration was quantified daily from planting to harvesting age for a Eucalyptus grandis stand using a soil water transfer model coupled with an ecophysiological forest model (MAESPA). The model was parameterized using an extensive data set and validated using time series of the soil water content down to a depth of 10 m and water‐table level, as well as evapotranspiration measured using eddy covariance. Fast root growth after planting provided access to large quantities of water stored in deep soil layers over the first 2 years. Eucalyptus roots reached the water‐table at a depth of 12 m after 2 years. Although the mean water withdrawal from depths of over 10 m amounted to only 5% of canopy transpiration from planting to a harvesting age of 5 years, the proportion of water taken up near the water‐table was much higher during dry periods. The water‐table rose from 18 to 12 m below‐ground over 2 years after the harvest of the previous stand and then fell until harvesting age as evapotranspiration rates exceeded the annual rainfall. Deep rooting is an efficient strategy to increase the amount of water available for the trees, allowing the uptake of transient gravitational water and possibly giving access to a deep water‐table. Deep soil layers have an important buffer role for large amounts of water stored during the wet season that is taken up by trees during dry periods. Our study confirms that deep rooting could be a major mechanism explaining high transpiration rates throughout the year in many tropical forests. Lay Summary

Process-based models are increasingly used to study agroecosystem interactions and N₂O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N₂O flux from three process-based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi-model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross-model bias to underestimate high magnitude daily and cumulative N₂O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH₄⁺, and soil NO₃−, which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N₂O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development.

Model–data comparisons of plant physiological processes provide an understanding of mechanisms underlying vegetation responses to climate. We simulated the physiology of a piñon pine–juniper woodland (Pinus edulis–Juniperus monosperma) that experienced mortality during a 5 yr precipitation-reduction experiment, allowing a framework with which to examine our knowledge of drought-induced tree mortality. We used six models designed for scales ranging from individual plants to a global level, all containing state-of-the-art representations of the internal hydraulic and carbohydrate dynamics of woody plants. Despite the large range of model structures, tuning, and parameterization employed, all simulations predicted hydraulic failure and carbon starvation processes co-occurring in dying trees of both species, with the time spent with severe hydraulic failure and carbon starvation, rather than absolute thresholds per se, being a better predictor of impending mortality. Model and empirical data suggest that limited carbon and water exchanges at stomatal, phloem, and below-ground interfaces were associated with mortality of both species. The model–data comparison suggests that the introduction of a mechanistic process into physiology-based models provides equal or improved predictive power over traditional process-model or empirical thresholds. Both biophysical and empirical modeling approaches are useful in understanding processes, particularly when the models fail, because they reveal mechanisms that are likely to underlie mortality. We suggest that for some ecosystems, integration of mechanistic pathogen models into current vegetation models, and evaluation against observations, could result in a breakthrough capability to simulate vegetation dynamics.

Aim: To investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUEt) and transpiration-based inherent water-use efficiency (IWUEt). Location: Global terrestrial ecosystems. Methods: We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Results: Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6 g C m⁻² mm⁻¹ lower than the simulations from four process-based models (2.0 ± 0.3 g C m⁻² mm⁻¹) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50° N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUEt shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUEt, the product of WUEt and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. Main conclusions: WUE, WUEt and IWUEt produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.

Niche shifts of nonnative plants can occur when they colonize novel climatic conditions. However, the mechanistic basis for niche shifts during invasion is poorly understood and has rarely been captured within species distribution models. We quantified the consequence of between-population variation in phenology for invasion of common ragweed (Ambrosia artemisiifolia L.) across Europe. Ragweed is of serious concern because of its harmful effects as a crop weed and because of its impact on public health as a major aeroallergen. We developed a forward mechanistic species distribution model based on responses of ragweed development rates to temperature and photoperiod. The model was parameterized and validated from the literature and by reanalyzing data from a reciprocal common garden experiment in which native and invasive populations were grown within and beyond the current invaded range. It could therefore accommodate between-population variation in the physiological requirements for flowering, and predict the potentially invaded ranges of individual populations. Northern-origin populations that were established outside the generally accepted climate envelope of the species had lower thermal requirements for bud development, suggesting local adaptation of phenology had occurred during the invasion. The model predicts that this will extend the potentially invaded range northward and increase the average suitability across Europe by 90% in the current climate and 20% in the future climate. Therefore, trait variation observed at the population scale can trigger a climatic niche shift at the biogeographic scale. For ragweed, earlier flowering phenology in established northern populations could allow the species to spread beyond its current invasive range, substantially increasing its risk to agriculture and public health. Mechanistic species distribution models offer the possibility to represent niche shifts by varying the traits and niche responses of individual populations. Ignoring such effects could substantially underestimate the extent and impact of invasions.

Vegetation phenology studies the periodic recurrence of plant life‐cycle events and is essential for understanding ecosystem responses to environmental changes. Remote sensing has become a key tool for monitoring phenological events on large spatial and temporal scales, primarily using vegetation indices like the Normalized Difference Vegetation Index (NDVI). However, current methods for extracting phenological metrics from NDVI data often fail to capture their biological and physiological significance, as they are predominantly based on statistical fitting functions. This study presents SWELL (Simulated Waves of Energy, Light, and Life), a process‐based phenology model that simulates the temporal NDVI profile, from leaf unfolding to dormancy release, based on species‐specific photothermal response functions. Tested on European beech, SWELL successfully reproduced seasonal Moderate Resolution Imaging Spectroradiometer NDVI patterns from 2012 to 2021 across ecoregions, matching the performance of a benchmark model and enabling consistent analysis of phenological phases' timing across biogeographic gradients. SWELL allows bridging the gap between remotely sensed phenology and the underlying ecophysiological processes. By overcoming current limitations in process‐based phenology modelling, SWELL may represent a novel tool for understanding and predicting vegetation phenology in the context of climate change.