Biology subjects

Stocker, B. D.

Publications and source records attributed to Stocker, B. D..

10 recordsLinked to original sources

bioRxiv2025

Diagnosing linearity along the carbon cascade in terrestrial biosphere models

Elevated carbon dioxide (eCO2) fertilises photosynthesis, driving an increase in terrestrial gross primary production (GPP). However, it is unclear how effectively increased GPP propagates along the "carbon (C) cascade" to increase net primary production (NPP) and vegetation C stocks (Cveg) in different plant compartments. Vegetation models simulate divergent C cycle projections and have been criticised for being overly photosynthesis-driven (source-driven), neglecting processes that lead to non-linear behaviour in response to the GPP increase, which may attenuate (or amplify) changes in NPP and vegetation C stocks. Here, we introduce an analytical framework to diagnose linearity (L) of the land C cycle as the ratio of relative changes in linked fluxes and pools and apply it to outputs from 16 models of the TRENDY v11 ensemble. We found widely varying patterns in L across models and for the different links. Six models showed a clear dominance of larger relative changes in NPP than in GPP in global simulations (LNPP:GPP >1 for >60% of gridcells), indicating increased carbon use efficiency under eCO2. Only three models had LNPP:GPP < 1 for >60% of gridcells. Four models showed a clear dominance of larger relative changes in steady-state Cveg than in NPP, while five models showed an opposite pattern - in both cases with a large spread of LCveg*:NPP across gridcells within models. Three models showed a larger relative increase in root C than in Cveg, while two models showed a clear dominance of the opposite pattern. Widely differing distributions of L across models and links reveal a strong influence of alternative process representations (nonlinear behaviour) in individual models. However, for all links, L deviations from 1 were roughly balanced across the model ensemble, leading to an overall linear behaviour of terrestrial C cycle representations.

ecology↗

bioRxiv2024

CN-model: A dynamic model for the coupled carbon and nitrogen cycles in terrestrial ecosystems

Reducing uncertainty in carbon cycle projections relies on reliable representations of interactions between the carbon and nutrient cycles. Here, we build on a set of principles and hypotheses, funded in established theoretical understanding and supported by empirical evidence, to formulate and implement a dynamic model of carbon-nitrogen cycle (C-N) interactions in terrestrial ecosystems. The model combines a representation of photosynthetic acclimation to the atmospheric environment with implications for canopy and leaf N, a representation of functional balance for modelling C allocation and growth different tissues, and a representation of plant N uptake based on a first principles-informed, simplified description of solute transport in the soil. Here, we provide a comprehensive description of the model and its implementation and demonstrate the stability and functionality of the model for simulating seasonal variations of ecosystem C and N fluxes and pools. This may provide a way forward for representing key ecosystem processes related to C and N cycling based on established theoretical concepts with strong empirical support.

ecology↗

bioRxiv2023

rsofun: A model-data integration framework for simulating ecosystem processes

Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse biotic and abiotic conditions. Systematically informing them with data is key for enhancing their predictive accuracy and estimating uncertainty. Here we present the Simulating Optimal FUNctioning {rsofun} R package, providing a computationally efficient and parallelizable implementation of the P-model for site-scale simulations of ecosystem photosynthesis, complemented with functionalities for Bayesian model-data integration and estimation of parameters and uncertainty. We describe a use case to demonstrate the package functionalities for modelling ecosystem gross CO2 uptake at one flux measurement site, including model sensitivity analysis, Bayesian parameter calibration, and prediction uncertainty estimation. {rsofun} lowers the bar of entry to ecosystem modelling and model-data integration and serves as an open-access resource for model development and dissemination.

ecology↗

bioRxiv2022

Tree growth enhancement drives a persistent biomass gain in unmanaged temperate forests

While enhanced tree growth over the last decades has been reported in forests across the globe, it remains unclear whether it drives persistent biomass increases of the stands, particularly in mature forests. Enhanced tree growth and stand-level biomass are often linked with a simultaneous increase in density-driven mortality and a reduction in tree longevity. Identifying empirical evidence regarding the balance between these processes is challenging due to the confounding effects of stand history, management, and environmental changes. Here, we investigate the link between growth and biomass via the shift in the negative relationship between average tree size and stand density (tree number). We find increasing stand density for a given tree size in unmanaged closed-canopy forests in Switzerland over the past six decades and a positive relationship between growth and stand density - qualitatively consistent with simulations by a mechanistic, cohort-resolving ecosystem model (LM3-PPA). Model simulations show that, in the absence of other disturbances, enhanced growth persistently increases biomass stocks despite simultaneous decreases in carbon residence time and tree longevity, independent of assumptions about the drivers of tree mortality. However, the magnitude of simulated changes critically depends on the shape of the mortality parameterizations. Our analyses reconcile reports of growth-induced reductions of tree longevity with model predictions of persistent biomass increases, and with our finding of a trend towards denser forests in response to growth - also in mature stands.

ecology↗

bioRxiv2022

Global terrestrial nitrogen uptake and nitrogen use efficiency

Plant biomass production (BP), nitrogen uptake (Nup) and their ratio, nitrogen use efficiency (NUE), must be quantified to understand how nitrogen (N) cycling constrains terrestrial carbon (C) uptake. But the controls of key plant processes determining Nup and NUE, including BP, C and N allocation, tissue C:N ratios and N resorption efficiency (NRE), remain poorly known. We compiled measurements from 804 forest and grassland sites and derived regression models for each of these processes with growth temperature, vapour pressure deficit, stand age, soil C:N ratio, fAPAR (remotely sensed fraction of photosynthetically active radiation absorbed by green vegetation) and growing-season average daily incident photosynthetic photon flux density (gPPFD) (effectively the seasonal concentration of light availability, which increases polewards) as predictors. An empirical model for leaf N was based on optimal photosynthetic capacity (a function of gPPFD and climate) and observed leaf mass-per-area. The models were used to produce global maps of Nup and NUE. Global BP was estimated as 72 Pg C/yr; Nup as 950 Tg N/yr; and NUE as 76 gC/gN. Forest BP was found to increase with growth temperature and fAPAR and to decrease with stand age, soil C:N ratio and gPPFD. Forest NUE is controlled primarily by climate through its effect on C allocation - especially to leaves, being richer in N than other tissues. NUE is greater in colder climates, where N is less readily available, because belowground allocation is increased. NUE is also greater in drier climates because leaf allocation is reduced. NRE is enhanced (further promoting NUE) in both cold and dry climates. These findings can provide observationally based benchmarks for model representations of C-N cycle coupling. State-of-the-art vegetation models in the TRENDY ensemble showed variable performance against these benchmarks, and models including coupled C-N cycling produced relatively poor simulations of Nup and NUE.

ecology↗

bioRxiv2022

Deep learning for satellite image forecasting of vegetation greenness

The advent of abundant Earth observation data enables the development of novel predictive methods for forecasting climate impacts on the state and health of terrestrial ecosystems. Here, we target the spatial and temporal variations of land surface reflectance and vegetation greenness, measuring the density of green vegetation and active foliage area, conditioned on current and past climate and the local topography. We train two alternative recurrent deep learning models that rely on convolutional layers for forecasting the spatially resolved deviation of surface reflectance across a heterogeneous landscape from a specified initial state (Baseline Framework). We demonstrate efficiency of the Baseline Framework with respect to training convergence speed. Using data from diverse ecosystems and land cover types across Europe and following a standardized model evaluation framework (EarthNet2021 Challenge), results indicate increased performance in predicting surface greenness during drought events of the models presented here, compared to currently published benchmarks. Our results demonstrate how deep learning methods enable early-warning of vegetation responses to the impacts of climatic extreme events, such as the drought-related loss of green foliage.

plant biology↗

bioRxiv2022

Acclimation of phenology relieves leaf longevity constraints in deciduous forests

Leaf phenology is key for regulating total growing season mass and energy fluxes. Long-term temporal trends towards earlier leaf unfolding are observed across Northern Hemisphere forests. Phenological dates also vary between years, whereby end-of-season (EOS) dates correlate positively with start-of-season (SOS) dates and negatively with growing season total net CO2 assimilation (Anet). These associations have been interpreted as the effect of a constrained leaf longevity or of premature carbon (C) sink saturation - with far-reaching consequences for long-term phenology projections under climate change and rising CO2. Here, we use multi-decadal ground and remote-sensing observations to show that the relationships between Anet and EOS are opposite at the interannual and the decadal time scales. A decadal trend towards later EOS persists in parallel with a trend towards increasing Anet - in spite of the negative Anet-EOS relationship at the interannual scale. This indicates that acclimation of phenology has enabled plants to transcend a constrained leaf longevity or premature C sink saturation over the course of several decades, leading to a more effective use of available light and a sustained extension of the vegetation CO2 uptake season over time.

ecology↗

bioRxiv2021

Global distribution of the rooting zone water storage capacity reflects plant adaptation to the environment

The rooting zone water storage capacity (S0) extends from the soil surface to the weathered bedrock (the Critical Zone) and determines land-atmosphere exchange during dry periods. Despite its importance to land-surface modeling, variations of S0 across space are largely unknown as they cannot be observed directly. We developed a method to diagnose global variations of S0 from the relationship between vegetation activity (measured by sun-induced fluorescence and by the evaporative fraction) and the cumulative water deficit (CWD). We then show that spatial variations in S0 can be predicted from the assumption that plants are adapted to sustain CWD extremes occurring with a return period that is related to the life form of dominant plants and the large-scale topographical setting. Predicted biome-level S0 distributions, translated to an apparent rooting depth (zr) by accounting for soil texture, are consistent with observations from a comprehensive zr dataset. Large spatial variations in S0 across the globe reflect adaptation of zr to the hydroclimate and topography and implies large heterogeneity in the sensitivity of vegetation activity to drought. The magnitude of S0 inferred for most of the Earths vegetated regions and particularly for those with a large seasonality in their hydroclimate indicates an important role for plant access to water stored at depth - beyond the soil layers commonly considered in land-surface models.

ecology↗

bioRxiv2021

Photosynthetic acclimation and sensitivity to short- and long-term environmental changes

O_LIThe future climate will be characterized by an increase in frequency and duration of drought and warming that exacerbates atmospheric evaporative demand. How trees acclimate to long-term soil moisture changes and whether these long-term changes alter trees sensitivity to short-term (day to months) variations of vapor pressure deficit (VPD) and soil moisture is largely unknown. C_LIO_LILeaf gas exchange measurements were performed within a long-term (17 years) irrigation experiment in a Scots pine-dominated forest in one of Switzerlands driest areas on trees in naturally dry (control), irrigated, and irrigation-stop (after 11 years of irrigation) conditions. C_LIO_LISeventeen years of irrigation increased photosynthesis (A) and stomatal conductance (gs) and reduced the gs sensitivity to increasing VPD but not to soil drying. Following irrigation-stop, gas exchange did not decrease immediately, but after three years, had decreased significantly in irrigation-stop trees. Vcmax and Jmax recovered after five years. C_LIO_LIThese results suggest that long-term release of soil drought reduces the sensitivity to atmospheric evaporative demand and that atmospheric constraints may play an increasingly important role in combination with soil drought. In addition, they suggest that structural adjustments lead to an attenuation of initially strong leaf-level acclimation to strong multiple-year drought. C_LI

plant biology↗

bioRxiv2020

Towards a unified theory of plant photosynthesis and hydraulics

The global carbon and water cycles are governed by the coupling of CO2 and water vapour exchanges through the leaves of terrestrial plants, controlled by plant adaptations to balance carbon gains and hydraulic risks. We introduce a trait-based optimality theory that unifies the treatment of stomatal responses and biochemical acclimation of plants to environments changing on multiple timescales. Tested with experimental data from 18 species, our model successfully predicts the simultaneous decline in carbon assimilation rate, stomatal conductance, and photosynthetic capacity during progressive soil drought. It also correctly predicts the dependencies of gas exchange on atmospheric vapour pressure deficit, temperature, and CO2. Model predictions are also consistent with widely observed empirical patterns, such as the distribution of hydraulic strategies. Our unified theory opens new avenues for reliably modelling the interactive effects of drying soil and rising atmospheric CO2 on global photosynthesis and transpiration.

plant biology↗