Explore the vast range of titles available.

We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Land Model version 4.1 (LM4.1), which builds on component and coupled model developments over 2013–2019 for the coupled carbon‐chemistry‐climate Earth System Model Version 4.1 (ESM4.1) simulation as part of the sixth phase of the Coupled Model Intercomparison Project. Analysis of ESM4.1/LM4.1 is focused on biophysical and biogeochemical processes and interactions with climate. Key features include advanced vegetation dynamics and multi‐layer canopy energy and moisture exchanges, daily fire, land use representation, and dynamic atmospheric dust coupling. We compare LM4.1 performance in the GFDL Earth System Model (ESM) configuration ESM4.1 to the previous generation component LM3.0 in the ESM2G configuration. ESM4.1/LM4.1 provides significant improvement in the treatment of ecological processes from GFDL's previous generation models. However, ESM4.1/LM4.1 likely overestimates the influence of land use and land cover change on vegetation characteristics, particularly on pasturelands, as it overestimates the competitiveness of grasses versus trees in the tropics, and as a result, underestimates present‐day biomass and carbon uptake in comparison to observations. Plain Language Summary The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new Land Model (LM4.1) as part of its 4th generation coupled model development. This model includes advances from the previous generation and introduces a new vegetation demography model, multi‐layer canopy, plant hydraulics, fire, and land use representation as well as dynamic atmospheric dust coupling. Coupled within an Earth System Model (ESM4.1), LM4.1 features an improved representation of many ecological processes from the previous generation of GFDL ESMs. Key Points A new land model LM4.1 is developed at the Geophysical Fluid Dynamics Laboratory (GFDL) for the next‐generation Earth System Model (ESM) ESM4.1 LM4.1 integrates age‐height structured vegetation dynamics, multi‐layer canopy‐soil‐snow energy exchanges, and prognostic fires and mineral dust ESM4.1/LM4.1 improves patterns of land surface climate and carbon cycle compared to the previous generation GFDL model ESM2G/LM3.0

Tree allometric relationships are widely employed for estimating forest biomass and production and are basic building blocks of dynamic vegetation models. In tropical forests, allometric relationships are often modeled by fitting scale-invariant power functions to pooled data from multiple species, an approach that fails to capture changes in scaling during ontogeny and physical limits to maximum tree size and that ignores interspecific differences in allometry. Here, we analyzed allometric relationships of tree height (9884 individuals) and crown area (2425) with trunk diameter for 162 species from the Barro Colorado Nature Monument, Panama. We fit nonlinear, hierarchical models informed by species traits – wood density, mean sapling growth, or sapling mortality – and assessed the performance of three alternative functional forms: the scale-invariant power function and the saturating Weibull and generalized Michaelis–Menten (gMM) functions. The relationship of tree height with trunk diameter was best fit by a saturating gMM model in which variation in allometric parameters was related to interspecific differences in sapling growth rates, a measure of regeneration light demand. Light-demanding species attained taller heights at comparatively smaller diameters as juveniles and had shorter asymptotic heights at larger diameters as adults. The relationship of crown area with trunk diameter was best fit by a power function model incorporating a weak positive relationship between crown area and species-specific wood density. The use of saturating functional forms and the incorporation of functional traits in tree allometric models is a promising approach for improving estimates of forest biomass and productivity. Our results provide an improved basis for parameterizing tropical plant functional types in vegetation models.

Uncertainty in the dynamics of the Amazon rainforest poses a critical challenge for accurately modeling the global carbon cycle. Current dynamic global vegetation models (DGVMs), which use one or two plant functional types for tropical rainforests, fail to capture observed biomass and mortality gradients in this region, raising concerns about their ability to predict forest responses to global change drivers. Here we assess the importance of spatially varying parameters to resolve ecosystem spatial heterogeneity in the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) DGVM. Using satellite observations of tree aboveground biomass (AGB), gross primary productivity (GPP), and biomass mortality rates, we optimized two key parameters: the alpha self-thinning (α), which controls tree mortality induced by light competition, and the nitrogen use efficiency of photosynthesis (η), which regulates GPP. The model incorporating spatially optimized α and η parameters successfully reproduces the spatial variability of AGB (R2 = 0.82), GPP (R2 = 0.79), and biomass mortality rates (R2 = 0.73) when compared to remote sensing observations in intact Amazon rainforests, whereas the model using spatially constant parameters has R2 values lower than 0.04 for all observations. Furthermore, the relationships between the optimized parameters and ecosystem traits, as well as climate variables, were evaluated using random forest regression. We found that wood density emerges as the most important determinant of α, which is in line with existing theory, while water deficit conditions significantly impact η. This study presents an efficient and accurate approach to enhancing the simulation of Amazonian carbon pools and fluxes in DGVMs by assimilating existing observational data, offering valuable insights for future model development and parameterization.

Representing biological nitrogen fixation (BNF) is an important challenge for coupled carbon (C) and nitrogen (N) land models. Initial representations of BNF in land models applied simplified phenomenological relationships. More recent representations of BNF are mechanistic and include the dynamic response of symbiotic BNF to N limitation of plant growth. However, they generally do not include the competitive dynamics between N-fixing and non-fixing plants, which is a key ecological mechanism that determines ecosystem-scale symbiotic BNF. Furthermore, asymbiotic BNF is generally not included in land models. Here, we present LM4.1-BNF, a novel representation of BNF (asymbiotic and symbiotic) and an updated representation of N cycling in the Geophysical Fluid Dynamics Laboratory Land Model 4.1 (LM4.1). LM4.1-BNF incorporates a mechanistic representation of asymbiotic BNF by soil microbes, a representation of the competitive dynamics between N-fixing and non-fixing plants, and distinct asymbiotic and symbiotic BNF temperature responses derived from corresponding observations. LM4.1-BNF makes reasonable estimations of major carbon (C) and N pools and fluxes and their temporal dynamics, in comparison to the previous version of LM4.1 with N cycling (LM3-SNAP) and to previous representations of BNF in land models generally (phenomenological representations and those without competitive dynamics between N-fixing and non-fixing plants and/or asymbiotic BNF) at a temperate forest site. LM4.1-BNF effectively reproduces asymbiotic BNF rate (13 kgNha-1yr-1) in comparison to observations (11 kgNha-1yr-1). LM4.1-BNF effectively reproduces the temporal dynamics of symbiotic BNF rate: LM4.1-BNF simulates a symbiotic BNF pulse in early succession that reaches 73 kgNha-1yr-1 at 15 years and then declines to ∼0 kgNha-1yr-1 at 300 years, similarly to observed symbiotic BNF, which reaches 75 kgNha-1yr-1 at 17 years and then declines to ∼0 kgNha-1yr-1 in late successional forests. As such, LM4.1-BNF can be applied to project the dynamic response of vegetation to N limitation of plant growth and the degree to which this will constrain the terrestrial C sink under elevated atmospheric CO2 concentration and other global change factors.

The use of CubeSats as space observation missions is now a reality. CubeSats are satellites in the category of small satellites (around 10 cm x 10 cm x 10 cm) that started as simply educational projects for students. But they soon spread out to scientific investigation and exploration. In this work, a low-energy transfer is designed and studied to transport these small satellites from low Earth orbit to orbit about the Moon. The theory behind low-energy transfers, as well as the computational methods, are described. A low-energy transfer is a transfer that exploits natural pathways in position-velocity space created by the forces of the Sun, Earth, and Moon acting on the CubeSat to reach the final target. The three-body problem in a rotating frame shows equilibrium points. Periodic orbits exist in the neighborhood of these equilibrium points. The low-energy transfer takes advantage of these periodic orbits and their stability properties using them as staging orbits. With precise maneuvers, a vehicle can be placed on a stable manifold of target periodic orbit such that it naturally travels to the target periodic orbits with little use of propellant. The transfers reduce the CubeSat propulsion requirements at the expense of transfer time. These transfers normally take 4 to 6 months to travel from a low orbit at the Earth to an orbit about the Moon. Following the procedures to design the transfer, this work develops, analyzes and compares a low-energy transfer with other transfer options. A case study is also shown to discuss the values obtained. The low-energy transfer is shown to reduce the propulsion requirements significantly in comparison to conventional direct transfers.

Alpine tree-line ecotones are characterized by marked changes at small spatial scales that may result in a variety of physiognomies. A set of alternative individual-based models was tested with data from four contrasting Pinus uncinata ecotones in the central Spanish Pyrenees to reveal the minimal subset of processes required for tree-line formation. A Bayesian approach combined with Markov chain Monte Carlo methods was employed to obtain the posterior distribution of model parameters, allowing the use of model selection procedures. The main features of real tree lines emerged only in models considering nonlinear responses in individual rates of growth or mortality with respect to the altitudinal gradient. Variation in tree-line physiognomy reflected mainly changes in the relative importance of these nonlinear responses, while other processes, such as dispersal limitation and facilitation, played a secondary role. Different nonlinear responses also determined the presence or absence of krummholz, in agreement with recent findings highlighting a different response of diffuse and abrupt or krummholz tree lines to climate change. The method presented here can be widely applied in individual-based simulation models and will turn model selection and evaluation in this type of models into a more transparent, effective, and efficient exercise.

The goal of successful anti-tumoural immunity is the development of long-term protective immunity to prevent relapse. Infiltration of tumours with CD8 + T cells with a resident memory (Trm) phenotype correlates with improved survival. However, the interplay of circulating CD8 + T cells and Trm cells remains poorly explored in tumour immunity. Using different vaccination strategies that fine-tune the generation of Trm cells or circulating memory T cells, here we show that, while both subsets are sufficient for anti-tumour immunity, the presence of Trm cells improves anti-tumour efficacy. Transferred central memory T cells (Tcm) generate Trm cells following viral infection or tumour challenge. Anti-PD-1 treatment promotes infiltration of transferred Tcm cells within tumours, improving anti-tumour immunity. Moreover, Batf3-dependent dendritic cells are essential for reactivation of circulating memory anti-tumour response. Our findings show the plasticity, collaboration and requirements for reactivation of memory CD8 + T cells subsets needed for optimal tumour vaccination and immunotherapy. Circulating memory cells include central memory T cells retaining the ability to enter the lymph nodes whereas tissue resident memory cells are confined to the parenchymal tissues. Here the authors explore the interplay between the two T-cell types and show that both cooperate in anti-tumour immunity.

Conventional type 1 dendritic cells (cDC1s) can sense tissue damage via DNGR-1, which binds F-actin exposed by necrotic cells. DNGR-1 activation favors cross-presentation, the process by which extracellular antigens are processed and presented to CD8 + T cells via major histocompatibility complex class I molecules. Del Fresno et al. studied mice lacking DNGR-1 and found that DNGR-1 also has anti-inflammatory effects (see the Perspective by Salazar and Brown). It inhibits the secretion of the chemokine CXCL2 by cDC1s, which, in turn, limits neutrophil recruitment. Thus, DNGR-1 connects cell-death sensing with a mechanism of damage control. Science , this issue p. 351 ; see also p. 292 A damage-sensing receptor hits the brakes to control the extent of tissue damage during infection, injury, and inflammation. Host injury triggers feedback mechanisms that limit tissue damage. Conventional type 1 dendritic cells (cDC1s) express dendritic cell natural killer lectin group receptor-1 (DNGR-1), encoded by the gene Clec9a , which senses tissue damage and favors cross-presentation of dead-cell material to CD8 + T cells. Here we find that DNGR-1 additionally reduces host-damaging inflammatory responses induced by sterile and infectious tissue injury in mice. DNGR-1 deficiency leads to exacerbated caerulein-induced necrotizing pancreatitis and increased pathology during systemic Candida albicans infection without affecting fungal burden. This effect is B and T cell–independent and attributable to increased neutrophilia in DNGR-1–deficient settings. Mechanistically, DNGR-1 engagement activates SHP-1 and inhibits MIP-2 (encoded by Cxcl2 ) production by cDC1s during Candida infection. This consequently restrains neutrophil recruitment and promotes disease tolerance. Thus, DNGR-1–mediated sensing of injury by cDC1s serves as a rheostat for the control of tissue damage, innate immunity, and immunopathology.

Garaude and colleagues show that innate immunological sensing of live bacteria by macrophages elicits transient adaptations to the electron-transport chain of mitochondria. Macrophages tightly scale their core metabolism after being activated, but the precise regulation of the mitochondrial electron-transport chain (ETC) and its functional implications are currently unknown. Here we found that recognition of live bacteria by macrophages transiently decreased assembly of the ETC complex I (CI) and CI-containing super-complexes and switched the relative contributions of CI and CII to mitochondrial respiration. This was mediated by phagosomal NADPH oxidase and the reactive oxygen species (ROS)-dependent tyrosine kinase Fgr. It required Toll-like receptor signaling and the NLRP3 inflammasome, which were both connected to bacterial viability–specific immune responses. Inhibition of CII during infection with Escherichia coli normalized serum concentrations of interleukin 1β (IL-1β) and IL-10 to those in mice treated with dead bacteria and impaired control of bacteria. We have thus identified ETC adaptations as an early immunological-metabolic checkpoint that adjusts innate immune responses to bacterial infection.