Ecosystem Simulation Research Articles

Study on driving factors of island ecosystem health and multi-scenario ecology simulation using ecological conservation and eco-friendly tourism for achieving sustainability

Maintaining ecosystem health on islands is essential for constructing an ecologically balanced civilization and supporting sustainable development of island tourism. This study provided a quantitative analysis of the ecosystem's evolution on Hainan Island from 2000 to 2020, meticulously investigating the interplay between biodiversity, tourism, and local economic activities on ecosystem health. Additionally, it simulated ecosystem health in 2030 across four distinct scenarios: ecological conservation, eco-friendly tourism, natural development, and economic growth. Finally, practical management suggestions were proposed for achieving multifaceted sustainable ecosystem development. The findings indicated: (1) Ecosystem health on Hainan Island exhibited an overall declining trend from 2000 to 2020, with a noticeable decrease moving from the central mountainous to the plains area. (2) Variations in ecosystem health were primarily shaped by natural geographic factors such as altitude and slope, though socio-economic factors and tourism development increasingly influenced these trends. In addition, natural environmental factors and tourism factors contributed positively to ecosystem health, while socio-economic factors had a negative effect. (3) Simulation predictions demonstrated that prioritizing ecological protection alongside promoting tourism can positively impact ecosystem health on Hainan Island, whereas exclusive emphasis on economic growth may jeopardize long-term sustainability. This study offers invaluable perspectives for conserving biodiversity, fostering socio-economic progress, and aligning ecological conservation with tourism development towards a harmonious multi-objective strategy.

Control of simulated ocean ecosystem indicators by biogeochemical observations

To protect marine ecosystems threatened by climate change and anthropic stressors, it is essential to operationally monitor ocean health indicators. These are metrics synthetizing multiple marine processes relevant to the users of operational services. Here we assess if selected ocean indicators simulated by operational models can be controlled (here meaning constrained effectively) by biogeochemical observations, by using a newly proposed methodological framework. The method consists in firstly screening the sensitivities of the indicators with respect to the initial conditions of the observable variables. These initial conditions are perturbed stochastically in Monte Carlo simulations of one-dimensional configurations of a multi-model ensemble. Then, the models are applied in three-dimensional ensemble assimilation experiments, where the reduction of the ensemble variance corroborates the controllability of the indicators by the observations. The method is applied for ten relevant ecosystem indicators (ranging from inorganic chemicals to plankton production), seven observation types (representing data from satellite and underwater platforms), and an ensemble of five biogeochemical models of different complexity, employed operationally by the European Copernicus Marine Service. We demonstrate that all the indicators are controlled by one or more types of observations. In particular, the indicators of phytoplankton phenology are controlled and improved by the merged observations from the surface ocean colour and chlorophyll profiles. Similar observations also control and reduce the uncertainty of the plankton community structure and production. However, the uncertainty of the trophic efficiency and POC increases when assimilating chlorophyll-a data, though observations were not available to assess whether that was due to a worsen model skill. We recommend that the assessment of controllability proposed here becomes a standard practice in designing operational monitoring, reanalysis and forecast systems, to ultimately provide the users of operational services with more precise estimates of ocean ecosystem indicators.

The MOOSE fluid properties module

The Fluid Properties module within the Multiphysics Object-Oriented Simulation Environment (MOOSE) is used to compute fluid properties for numerous applications, ranging from nuclear reactor thermal hydraulics to geothermal energy. Those applications drove the development of the module to enable numerous different fluid equations of states, property lookups with primitive and conserved flow variable to cater to pressure and density-driven solvers, and an object-oriented design facilitating expansion and maintenance. Each fluid property is implemented in its own class but inherits capabilities such as automatic differentiation, automated out-of-bounds handling or variable conversion capabilities. This paper presents the module, its design, its user and developer interface, its content in terms of fluids and properties, and several of its applications showing its major role in the MOOSE simulation ecosystem. Program summaryProgram title: MOOSE Fluid Properties moduleCPC Library link to program files:https://doi.org/10.17632/cwzhsyp6pd.1Developer's repository link:https://github.com/idaholab/moose/tree/next/modules/fluid_propertiesLicensing provisions: LGPLProgramming language: C++, PythonNature of problem: The simulation of thermal hydraulics of advanced nuclear reactor systems, such as heat pipe micro-reactors or molten-salt cooled pebble bed reactors, requires a wide variety of discretizations of the fluid flow equations, from 1D thermal hydraulics to computational fluid dynamics at various levels of fidelity, with a wide variety of coolants. Applications are developed within the MOOSE C++ framework by Argonne and Idaho National Laboratories to simulate these reactors for research and design purposes. These applications (Sockeye, SAM, others) rely on MOOSE for the computation of fluid properties. The fluid properties module contains properties for most advanced nuclear reactor coolants, including an interface to the Molten Salt Thermodynamics Database (MSTDB) developed by Oak Ridge National Laboratory. Single phase, two phase, and gas mixtures fluid properties are computed by the module.Solution method: The fluid properties module includes numerous numerical methods to support the wide range of applications, notably forward automatic differentiation, conversion methods between pressure and density-driven variable sets, spline-based table lookups which are the current state of the art for the fast computation of fluid properties. The integration with MOOSE facilitates uncertainty quantification with regards to the fluid properties and optimization studies with regards to the fluid composition.

FastFlow: GPU Acceleration of Flow and Depression Routing for Landscape Simulation

AbstractTerrain analysis plays an important role in computer graphics, hydrology and geomorphology. In particular, analyzing the path of material flow over a terrain with consideration of local depressions is a precursor to many further tasks in erosion, river formation, and plant ecosystem simulation. For example, fluvial erosion simulation used in terrain modeling computes water discharge to repeatedly locate erosion channels for soil removal and transport. Despite its significance, traditional methods face performance constraints, limiting their broader applicability.In this paper, we propose a novel GPU flow routing algorithm that computes the water discharge in 𝒪(log n) iterations for a terrain with n vertices (assuming n processors). We also provide a depression routing algorithm to route the water out of local minima formed by depressions in the terrain, which converges in 𝒪(log2 n) iterations. Our implementation of these algorithms leads to a 5× speedup for flow routing and 34 × to 52 × speedup for depression routing compared to previous work on a 10242 terrain, enabling interactive control of terrain simulation.

Air Canvas Using MediaPipe for Computer Vision in Unity 3D Hand Tracking

In this paper, we present the implementation of an Air Canvas using MediaPipe for Computer Vision within a 3D environment using the Unity Game Engine. In our previous work, we found that, regardless of the initial parameters, simulations often led to rapid extinctions. In this model, we implemented an Air Canvas and Computer Vision system integrated with Unity’s 3D World. Our goal was to achieve system stabilization, long-term operation, and more realistic simulation by incorporating 3D evolution. Using the Unity Game Engine, we created and managed a closed 3D ecosystem environment, either based on artificial or real-world maps. This simulation of ecosystems and the analysis of the data generated can serve as a starting point for further research, particularly in sustainability. Our system is openly accessible, allowing users to customize and upload their parameters, maps, and objects, and define inheritance and behavioural patterns, enabling them to test their hypotheses based on the data generated. The goal of this article is not to create and validate a model but to provide an IT tool. For evolutionary researchers, the system allows the creation and presentation of simulations, including animated conference presentations for enhanced visualization and engagement. The use of 3D simulation is particularly valuable for educational purposes, engaging students and increasing their interest in 3D interactive worlds. Students can observe how ecosystems behave, how natural selection supports adaptability, and how competition impacts species.

GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPPHLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPPMODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPPEC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide.

Using System‐Inspired Metrics to Improve Water Quality Prediction in Stratified Lakes

AbstractDespite the growing use of Aquatic Ecosystem Models for lake modeling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. Calibration is generally based on direct data comparison of observed versus modeled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model's ability to capture system‐scale behavior that is not easily perceivable in observations, but which may be important for resource management. The aim of this study is to compare the performance of “naïve” calibration and a “system‐inspired” calibration, an approach that augments the standard state‐based calibration with a range of system‐inspired metrics (e.g., thermocline depth, metalimnetic oxygen minima), to increase the coherence between the simulated and natural ecosystems. A coupled physical‐biogeochemical model was applied to a focal site to simulate two key state‐variables: water temperature and dissolved oxygen. The model was calibrated according to the new system‐inspired modeling convention, using formal calibration techniques. There was an improvement in the simulation using parameters optimized on the additional metrics, which helped to reduce uncertainty predicting aspects of the system relevant to reservoir management, such as the occurrence of the metalimnetic oxygen minima. Extending the use of system‐inspired metrics when calibrating models has the potential to improve model fidelity for capturing more complex ecosystem dynamics.

Large potential impacts of marine heatwaves on ecosystem functioning.

Ocean warming is driving significant changes in the structure and functioning of marine ecosystems, shifting species' biogeography and phenology, changing body size and biomass and altering the trophodynamics of the system. Particularly, extreme temperature events such as marine heatwaves (MHWs) have been increasing in intensity, duration and frequency. MHWs are causing large-scale impacts on marine ecosystems, such as coral bleaching, mass mortality of seagrass meadows and declines in fish stocks and other marine organisms in recent decades. In this study, we developed and applied a dynamic version of the EcoTroph trophodynamic modelling approach to study the cascading effects of individual MHW on marine ecosystem functioning. We simulated theoretical user-controlled ecosystems and explored the consequences of various assumptions of marine species mortality along the food web, associated with different MHW intensities. We show that an MHW can lead to a significant biomass reduction of all consumers, with the severity of the declines being dependent on species trophic levels (TLs) and biomes, in addition to the characteristics of MHWs. Biomass of higher TLs declines more than lower TLs under an MHW, leading to changes in ecosystem structure. While tropical ecosystems are projected to be sensitive to low-intensity MHWs, polar and temperate ecosystems are expected to be impacted by more intense MHWs. The estimated time to recover from MHW impacts is twice as long for polar ecosystems and one-third longer for temperate biomes compared with tropical biomes. This study highlights the importance of considering extreme weather events in assessing the effects of climate change on the structures and functions of marine ecosystems.

A dataset of gridded phenology of woody plants in Europe from 1951 to 2021

Plant phenology records the timing of plant cyclic growth events, and stand as one of the most important indicators of climate change. The monitoring and research of plant phenology is of great significance for understanding the response of ecosystems to global changes and simulating the material and energy balance of terrestrial ecosystems. Based on the ground observation phenological data of six representative woody plants compiled by the Pan European Phenology Project (PEP725) in the past 70 years, this paper employed three phenology models (Unichill, Unified and TSC) to predict and upscale the phenology data on the continental scale, and prepared a dataset of gridded phenology of woody plants in Europe. This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1 ° and a temporal resolution of one day. The quality assessment of the grid phenology data shows that the average error for FLD and FFD is 7.9 and 7.6 days respectively, which are close to simulation errors of spring phenology observed in other regional-scale studies, revealing the high accuracy of the data. This dataset can better characterize the temporal and spatial patterns of plant phenology at a continental scale in Europe, and provide an effective verification approach for plant phenology products from other sources. Furthermore, it can offer valuable data support for research on global change and terrestrial ecosystem simulation.

Characterization of a Simulated Martian Regolith Ecosystem by Proton Nuclear Magnetic Resonance (NMR) Relaxometry and Fourier Transform Infrared (FT-IR) and Visible (VIS)-Near-Infrared (NIR) Spectroscopies

Of the various Martian soil simulant mixes developed by NASA and Jet Propulsion Laboratory (JPL), three types were chosen that have the closest features to those of the Martian regolith to be analyzed. The characterization of the Martian regolith types was performed the advanced methods of 1H nuclear magnetic resonance (NMR) relaxometry, Fourier transform infrared (FT-IR) spectroscopy and visible (VIS)-near-infrared (NIR) spectroscopy, as well as classical methods such as pH, the electrical conductivity, and the total dissolved solids. These analyses showed that trace water, nitrogen, phosphorus and potassium are present in the Martian regolith simulant. A Martian Garden has been built with the Martian soils, in which various vegetables have been seeded. Among the Fourier Transform infrared (FT-IR) spectra acquired for the plants, a high degree of similarity was observed, which indicates that the substrate ‘Martian regolith simulant of terrestrial soil’ does not significantly influence the structure of the radish, peas and bean leaves, stems, or roots. Nevertheless, the results of 1D 1H nuclear magnetic resonance (NMR) relaxometry indicate that the substrate presents a high influence on the water dynamics in plant pores at the level of roots, stems and leaves and in bound water. A Marsarium was designed and built, where all types of Martian and terrestrial soils were introduced, together with a family of ants. The ants adapted to the imposed conditions, as they dug tunnels in the soils.

Effect of Lycium barbarum polysaccharide on osteoblast proliferation and differentiation in postmenopausal osteoporosis

ObjectiveWe aimed to investigate the effect of Lycium barbarum polysaccharide (LBP) on the proliferation and differentiation of osteoblasts in postmenopausal individuals with osteoporosis using in vitro cell experiments. MethodsWe assessed the effect of long-term LBP consumption on the intestinal metabolites of individuals using a simulation of the human intestinal microbiota ecosystem. We also tested the capacity of LBP in proliferating MC3T3-E1 cells using the cell counting kit-8 (CCK-8) method and analyzed the effect of intestinal metabolites on the osteogenic differentiation of MC3T3-E1 cells by testing bone metabolism viability with relevant indicators. ResultsThe level of short-chain fatty acids (SCFAs) significantly increased (p < 0.05), and the concentrations of acetic acid, propionic acid, and butyric acid all showed an upward trend after the treatment using LBP. At appropriate concentrations, the fermentation supernatant can enhance osteoblast proliferation by significantly increasing the active expression of bone-alkaline phosphatase (B-ALP) and osteocalcin (OCN) in osteoblasts (p < 0.05). ConclusionBy modulating the metabolites of intestinal microbiota, production of SCFAs, the prebiotic properties of LBP can enhance osteoblast differentiation through in vitro simulation experiment and cell-based assay.

Unveiling the influence of a probiotic combination of Heyndrickxia coagulans and Lacticaseibacillus casei on healthy human gut microbiota using the TripleSHIME® system

Probiotics are host-friendly microorganisms that can have important health benefits in the human gut microbiota as dietary supplements. Maintaining a healthy gut microbial balance relies on the intricate interplay among the intestinal microbiota, metabolic activities, and the host's immune response. This study aims to explore if a mixture of Heyndrickxia coagulans [ATB-BCS-042] and Lacticaseibacillus casei [THT-030-401] promotes in vitro this balance in representative gut microbiota from healthy individuals using the Triple-SHIME® (Simulation of the Human Intestinal Microbial Ecosystem). Metataxonomic analysis of the intestinal microbes revealed that the probiotic mix was not causing important disruptions in the biodiversity or microbial composition of the three simulated microbiota. However, some targeted populations analyzed by qPCR were found to be disrupted at the end of the probiotic treatment or after one week of washout. Populations such as Cluster IV, Cluster XVIa, and Roseburia spp., were increased indicating a potential gut health-promoting butyrogenic effect of the probiotic supplementation. In two of the systems, bifidogenic effects were observed, while in the third, the treatment caused a decrease in bifidobacteria. For the health-detrimental biomarker Escherichia-Shigella, a mild decrease in all systems was observed in the proximal colon sections, but these genera were highly increased in the distal colon sections. By the end of the washout, Bacteroides-Prevotella was found consistently boosted, which could have inflammatory consequences in the intestinal context. Although the probiotics had minimal influence on most quantified metabolites, ammonia consistently decreased after one week of daily probiotic supplementation. In reporter gene assays, aryl hydrocarbon receptor (AhR) activation was favored by the metabolic output obtained from post-treatment periods. Exposure of a human intestinal cell model to fermentation supernatant obtained after probiotic supplementation induced a trend to decrease the mRNA expression of immunomodulatory cytokines (IL-6, IL-8). Overall, with some exceptions, a positive impact of H. coagulans and L. casei probiotic mix was observed in the three parallel experiments, despite inter-individual differences. This study might serve as an in vitro pipeline for the impact assessment of probiotic combinations on the human gut microbiota.

Challenges of Including Wet Grasslands with Variable Groundwater Tables in Large-Area Crop Production Simulations

Large-scale assessments of agricultural productivity necessitate integrated simulations of cropland and grassland ecosystems within their spatiotemporal context. However, simultaneous simulations face limitations due to assumptions of uniform species distribution. Grasslands, particularly those with shallow groundwater tables, are highly sensitive to water availability, undergoing rapid species composition changes. We hypothesised that predicting above-ground biomass (AGB) remains challenging due to these dynamic responses. Ten years of data from four lysimeters at a German wet grassland site, with varying water table treatments, was utilised to test this hypothesis. Correlation analysis revealed a strong positive indirect effect of the water regime on AGB, with a one-year time lag (r = 0.97). The MONICA model initially exhibited fair agreement (d = 0.69) in simulating Leaf-Area-Index (LAI) but performed poorly in replicating AGB (d = 0.3). After removing the species composition change effect from the LAI and AGB datasets, the simulation notably improved, with the overall relative root mean square error (rRMSE) of AGB decreasing from 1.55 to 0.90 between the first and second simulations. This demonstrates MONICA’s ability to predict grass growth patterns amidst changing water supply levels for constant species composition. However, it needs a competition model to capture biomass growth changes with varying water supply.

An Application of Innovative Algorithm of Integrated Social Network Analysis with Statistical Lstm Chain Network Analysis (Slstm-Cna) for Entrepreneurial Team Member Selection

Selecting the right members for an entrepreneurial team is a critical step in ensuring the success and viability of a startup venture. Each team member brings unique skills, experiences, and perspectives that contribute to the overall vision and execution of the business idea. The factors such as cultural fit, adaptability, and a shared commitment to the venture's mission and values can foster cohesion and synergy within the team. This paper presents an innovative algorithm for the selection of entrepreneurial team members, Leveraging insights from social network dynamics and LSTM-based predictive modeling, the algorithm aims to identify individuals with the highest potential for contributing to the success of a startup venture. By analyzing social network structures, the algorithm identifies key influencers and connectors within professional networks, thereby facilitating the identification of candidates who possess valuable connections and collaborative capacities. Furthermore, SLSTM-CNA enables the algorithm to forecast future network trends and dynamics, aiding in the selection of team members who can adapt and thrive in evolving entrepreneurial ecosystems. simulated entrepreneurial ecosystem, the algorithm identified individuals with the highest potential for contributing to startup success based on various criteria, including network centrality, connectivity, and predictive network dynamics. For instance, individuals with a centrality score exceeding 0.7 demonstrated a 25% higher likelihood of facilitating valuable connections critical for business growth. Additionally, candidates identified through SLSTM-CNA exhibited a 30% increase in adaptability to changing network structures compared to those selected through conventional methods

A comprehensive evaluation of hydrological processes in a second‐generation dynamic vegetation model

AbstractThe global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere–hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund‐Potsdam‐Jena General Ecosystem Simulator (LPJ‐GUESS), using a broad range of the latest available global observation‐based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ‐GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ‐GUESS accurately captures both inter‐ and intra‐annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ‐GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML‐V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ‐GUESS aligns with ESA‐CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use.

Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm

Abstract. The processes responsible for methane (CH4) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) is one such model that allows quantification and understanding of the natural wetland CH4 fluxes at various scales, ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of the CH4 production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions highlight the need for a calibrated and optimised version of LPJ-GUESS. In this study, we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis–Hastings (MH)-based Markov chain Monte Carlo (MCMC) algorithm to improve predictions of CH4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method on uncertain parameters allows for a greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with a reduced risk of the sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period from 2005 to 2014 from the Siikaneva wetlands in Southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH4 flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to quantitatively assess the interrelationship between objective function choices, parameter identifiability, and data support. The experiment using real observations from Siikaneva resulted in a reduction in the root-mean-square error (RMSE), from 0.044 to 0.023 gC m−2 d−1, and a 93.89 % reduction in the cost function value. As a part of this work, knowledge about how CH4 data can constrain the parameters and processes is derived. Although the optimisation is performed based on a single site's flux data from Siikaneva, the algorithm is useful for larger-scale multi-site studies for a more robust calibration of LPJ-GUESS and similar models, and the results can highlight where model improvements are needed.

Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model

Subtropical forests have strong carbon sequestration potential; however, the spatiotemporal patterns of their carbon sink are unclear. The BIOME-BGC model is a powerful tool for forest carbon sink estimation while the numerous parameters, as well as the localization, limit their application. This study takes three typical subtropical forests (evergreen broadleaf forest, EBF; evergreen needleleaf forest, ENF; and bamboo forest, BF) in China as examples, assesses the sensitivity of 43 ecophysiological parameters in the BIOME-BGC model both by the Morris method and the extended Fourier amplitude sensitivity test (EFAST), and then evaluates the net ecosystem productivity (NEP) estimation accuracy based on the dataset of the fiveFi long-term carbon flux sites of those three typical forests from 2000 to 2015. The results showed that (1) both sensitivity analysis methods can effectively screen out important parameters affecting NEP simulation while the Morris method is more computationally efficient and the EFAST is better in the quantitative evaluation of sensitivity. (2) The highly sensitive parameters obtained using the two methods are basically the same; however, their importance varies across sites and vegetation types, e.g., the most sensitive parameters are k for the EBF and ENF and Ract25 for the BF, respectively. (3) The optimized parameters successfully improved the NEP simulation accuracy in subtropical forests, with average correlation coefficients increased by 25.19% and normalized root mean square error reduced by 21.74% compared with those simulated by original parameters. This study provides a theoretical basis for the optimization of process model parameters and important technical support for accurate NEP simulations of subtropical forest ecosystems.

Frost matters: incorporating late-spring frost into a dynamic vegetation model regulates regional productivity dynamics in European beech forests

Abstract. Late-spring frost (LSF) is a critical factor influencing the functioning of temperate forest ecosystems. Frost damage in the form of canopy defoliation impedes the ability of trees to effectively photosynthesize, thereby reducing tree productivity. In recent decades, LSF frequency has increased across Europe, likely intensified by the effects of climate change. With increasing warming, many deciduous tree species have shifted towards earlier budburst and leaf development. The earlier start of the growing season not only facilitates forest productivity but also lengthens the period during which trees are most susceptible to LSF. Moreover, recent forest transformation efforts in Europe intended to increase forest resilience to climate change have focused on increasing the share of deciduous species in forests. To assess the ability of forests to remain productive under climate change, dynamic vegetation models (DVMs) have proven to be useful tools. Currently, however, most state-of-the-art DVMs do not model processes related to LSF and the associated impacts. Here, we present a novel LSF module for integration with the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS). This new model implementation, termed LPJ-GUESS-FROST, provides the ability to directly attribute simulated impacts on forest productivity dynamics to LSF. We use the example of European beech, one of the dominant deciduous species in central Europe, to demonstrate the functioning of our novel LSF module. Using a network of tree-ring observations from past frost events, we show that LPJ-GUESS-FROST can reproduce productivity reductions caused by LSF. Further, to exemplify the effects of including LSF dynamics in DVMs, we run LPJ-GUESS-FROST for a study region in southern Germany for which high-resolution climate observations are available. Here, we show that modeled LSF plays a substantial role in regulating regional net primary production (NPP) and biomass dynamics, emphasizing the need for LSF to be more widely accounted for in DVMs.

Geographic Information System (GIS) Application in Soil Fertility Management: A Review

As the global population expected to reach 10 billion by 2050 and arable land decreases, innovative soil management strategies are required to maximize agricultural productivity while preserving natural resources. GIS, as a powerful technology, offers a comprehensive solution by enabling the collection, storage, analysis, and visualization of spatial data for agricultural purposes. By integrating GIS with sensors, communication technologies, and data analytics, precision agriculture is possible, allowing for efficient use of agricultural inputs and reduced environmental impact. GIS technology facilitates the simulation and modeling of biological ecosystems, providing valuable insights for sustainable soil management. It has diverse applications in agriculture, ranging from soil nutrient mapping to yield estimations and runoff detection. GIS works by overlaying various layers of information onto digital maps, allowing for the identification of relationships and patterns that are crucial for optimizing soil fertility. Detailed maps created through GIS highlight the distribution of soil nutrients, pH levels, and moisture content, enabling informed decision-making on fertilizer use, irrigation techniques, and land management practices. By harnessing the power of GIS, farmers and land managers can make data-driven decisions that maximize agricultural productivity, conserve resources, and protect soil health for future generations. The integration of GIS with other technologies further enhances its effectiveness in soil fertility management. Overall, GIS is a valuable tool for understanding and managing soil fertility, contributing to sustainable agriculture and food security.

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM‐FATES‐CNP)

AbstractWe present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon‐nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre‐existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant‐soil dynamics for a large number of size‐ and functional‐type‐resolved plant cohorts within a time‐since‐disturbance‐resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high‐light‐availability canopy positions allocate more carbon to fine roots than plants in low‐light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon‐nutrient coupling with vegetation demography within Earth system models.