Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning.

Distinguishing gross primary productivity (GPP) into sunlit (GPPsu) and shaded (GPPsh) components is critical for understanding the carbon exchange between the atmosphere and terrestrial ecosystems under climate change. Recently, the two-leaf light use efficiency (TL-LUE) model has proven effective for simulating global GPPsu and GPPsh. However, no known physical method has focused on integrating the overall constraint of intricate environmental factors on photosynthetic capability, and seasonal differences in the foliage clumping index (CI), which most likely influences GPP estimation in LUE models. Here, we propose the TL-CRF model, which uses the random forest technique to integrate various environmental variables, particularly for terrestrial water storage (TWS), into the TL-LUE model. Moreover, we consider seasonal differences in CI at a global scale. Based on 267 global eddy covariance flux sites, we explored the functional response of vegetation photosynthesis to key environmental factors, and trained and evaluated the TL-CRF model. The TL-CRF model was then used to simulate global eight-day GPP, GPPsu, and GPPsh from 2002 to 2020. The results show that the relative prediction error of environmental stress factors on the maximum LUE is reduced by approximately 52 % when these factors are integrated via the RF model. Thus the accuracy of global GPP estimation (R2 = 0.87, RMSE = 0.94 g C m-2 d-1, MAE = 0.61 g C m-2 d-1) in the TL-CRF model is greater than that (R2 = 0.76, RMSE = 2.18 g C m-2 d-1, MAE = 1.50 g C m-2 d-1) in the TL-LUE model, although this accuracy awaits further investigation among the released GPP products. TWS exerts the greatest control over ecosystem photosynthesis intensity, making it a suitable water indicator. Furthermore, the results confirm an optimal minimum air temperature for photosynthesis. Overall, these findings indicate a promising method for producing a new global GPP dataset, advancing our understanding of the dynamics and interactions between photosynthesis and environmental factors.

Reshaping the spatiotemporal patterns of temporal stability of forage nutrition quality in alpine grasslands of the Qinghai-Tibet Plateau: Spatial homogeneity, overall decline and localized increases.

There is increasing evidence that forage nutrition quality is becoming more unstable over time due to climate change and/or human activities. However, there are limited numbers of studies at the regional scale exploring the spatiotemporal patterns and driving mechanisms of temporal stability of nutrition quality. Therefore, this study quantified the spatiotemporal patterns of temporal stability of forage nutrition quality in alpine grasslands of the Qinghai-Tibet Plateau under the singular or combined influences of climate change and human activities in 2000-2020. Temporal stability of forage nutrition quality displayed obvious spatiotemporal patterns, with human activities altering the impact of climate change on these spatiotemporal patterns. Under combined effects of climate change and human activities, spatial average values of temporal stability of crude protein (CP), ash (Ash), ether extract (EE), water-soluble carbohydrates (WSC), acid detergent fiber (ADF), and neutral detergent fiber (NDF) decreased by 13.54 %, 7.40 %, 9.02 %, 17.78 %, 9.20 %, and 7.28 % across the whole grasslands, respectively. However, 39.43 %, 45.72 %, 42.98 %, 37.82 %, 42.27 %, and 43.50 % areas showed increasing trends for the temporal stability of CP, Ash, EE, WSC, ADF and NDF, respectively. Climate change predominated 46.15 %, 44.46 %, 44.22 %, 47.32 %, 28.68 %, and 45.31 % of the relative change of temporal stability of CP, Ash, EE, WSC, ADF, and NDF, but human activities had higher influence for 53.82 %, 55.53 %, 55.77 %, 52.55 %, 71.30 %, and 54.68 % of grasslands, respectively. Therefore, the spatial patterns of temporal stability of forage nutrition quality were shifting towards homogeneity, with an overall decrease in temporal stability but localized increases in alpine grasslands of the Qinghai-Tibet Plateau. The effects of climate change and human activities on forage nutrition quality were not always synergistic. The trade-off between nutrition quality and its temporal stability did not always exist, but varied with geographic position.

The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks.

Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot's control unit, i.e., as a cyborg's brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites ("n-sites") of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites ("the vitals") crucially depend on the samplings of three distributions: (1) the network distribution of neuronal excitability, (2) the distribution of connections between neurons of the network, and (3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models.

The state of nitrogen in rivers and streams across sub-Saharan Africa.

The nutrient status of rivers and streams is less researched in sub-Saharan Africa than in many other inhabited regions of the world. Given the expected population growth, intensification of agriculture, increased pressure on natural ecosystems and projected climate change in sub-Saharan Africa, it is crucial to quantify and understand drivers behind spatiotemporal patterns of nitrogen concentrations and loads in rivers and streams. Such knowledge can support sustainable management of water resources with the goal to provide clean water, create and maintain healthy ecosystems and prevent excessive pollution of water resources with nitrogen compounds, as is found in large parts of North America, Europe and Asia. This review provides a synthesis of the current available data from peer-reviewed literature (n = 243) on particulate and dissolved nitrogen in rivers and streams in sub-Saharan Africa, looking into seasonal and land cover-related differences. The review shows that data on nitrogen concentrations in rivers and streams is available for 32 out of the 48 countries (67 %) in sub-Saharan Africa, highlighting large data gaps given the size of the region. Differences in nitrogen concentrations between land cover types are reported, with highest median total nitrogen (3.9 mg N L-1) and nitrate (1.2 mg N L-1) concentrations observed at sites characterised by settlement and industry. In contrast, natural land cover types, like forest, have higher median (N:P) ratios (> 14.6) than cropland and urban areas (< 12.0). The analysis of paired samples from dry and wet seasons reveals varying effects of seasonality on the concentration of different nitrogen compounds between land cover types. However, the processes driving these spatiotemporal differences are still poorly understood. These findings highlight the need for a targeted research agenda for Africa to advance our understanding of the role of rivers and streams in nitrogen cycling in different ecosystems and their interaction with anthropogenic and natural drivers of change.

Characterizing Complex Spatiotemporal Patterns from Entropy Measures.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method for classifying spatiotemporal processes based on entropy measurements. To test and validate the method, we selected five classes of similar processes related to the evolution of random patterns: (i) white noise; (ii) red noise; (iii) weak turbulence from reaction to diffusion; (iv) hydrodynamic fully developed turbulence; and (v) plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon permutation entropy (SHp) and a new approach based on Tsallis Spectral Permutation Entropy (Sqs). Notably, our observations reveal the segregation of reaction terms in this SHp×Sqs space, a result that identifies specific sectors for each class of dynamic process, and it can be used to train machine learning models for the automatic classification of complex spatiotemporal patterns.

Dynamics of neural fields with exponential temporal kernel.

We consider the standard neural field equation with an exponential temporal kernel. We analyze the time-independent (static) and time-dependent (dynamic) bifurcations of the equilibrium solution and the emerging spatiotemporal wave patterns. We show that an exponential temporal kernel does not allow static bifurcations such as saddle-node, pitchfork, and in particular, static Turing bifurcations. However, the exponential temporal kernel possesses the important property that it takes into account the finite memory of past activities of neurons, which Green's function does not. Through a dynamic bifurcation analysis, we give explicit bifurcation conditions. Hopf bifurcations lead to temporally non-constant, but spatially constant solutions, but Turing-Hopf bifurcations generate spatially and temporally non-constant solutions, in particular, traveling waves. Bifurcation parameters are the coefficient of the exponential temporal kernel, the transmission speed of neural signals, the time delay rate of synapses, and the ratio of excitatory to inhibitory synaptic weights.

Spatiotemporal patterns of surface ozone exposure inequality in China.

Rising surface ozone (O3) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O3 for all subgroups has experienced a significant uptick, surpassing the average O3 concentration (118 µg/m3). Notably, the middle-aged demographic exhibited the highest O3 exposure level at 135.7 µg/m3, which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O3 exposure. In terms of urban-rural structure, urban groups in all regions had higher levels of exposure to O3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.

Spatiotemporal patterns of the pregnancy microbiome and links to reproductive disorders.

The microbiome of females undergoes extensive remodeling during pregnancy, which is likely to have an impact on the health of both mothers and offspring. Nevertheless, large-scale integrated investigations characterizing microbiome dynamics across key body habitats are lacking. Here, we performed an extensive meta-analysis that compiles and analyzes microbiome profiles from  >10,000 samples across the gut, vagina, and oral cavity of pregnant women from diverse geographical regions. We have unveiled unexpected variations in the taxonomic, functional, and ecological characteristics of microbial communities throughout the course of pregnancy. The gut microbiota showed distinct trajectories between Western and non-Western populations. The vagina microbiota exhibited fluctuating transitions at the genus level across gestation, while the oral microbiota remained relatively stable. We also identified distinctive microbial signatures associated with prevalent pregnancy-related disorders, including opposite variations in the oral and gut microbiota of patients with gestational diabetes and disrupted microbial networks in preterm birth. This study establishes a comprehensive atlas of the pregnancy microbiome by integrating multidimensional datasets and offers foundational insights into the intricate interplay between microbes and host factors that underlie reproductive health.

Spatiotemporal patterns of net primary productivity of subtropical forests in China and its response to drought.

Net primary productivity (NPP) is an important indicator used to evaluate the carbon sequestration capacity of forest ecosystems. Subtropical forest ecosystems play an indispensable role in maintaining the global carbon balance, while frequently occurring drought events in recent years have seriously damaged their productivity. However, the spatiotemporal patterns of NPP, as well as its response to drought, remain uncertain. In this study, the multiscale drought characteristics in subtropical China during 1981-2015 were analyzed based on the standardized precipitation evapotranspiration index. Then, simulated and analyzed the spatiotemporal NPP of subtropical forests by the boreal ecosystem productivity simulator model. Finally, the response of NPP to drought was analyzed based on multiple statistical indices. The results show that most regions in subtropical China experienced mild and moderate drought during 1981-2015. In particular, the extent of drought severity has shown a noticeable increasing trend after 2000. The forest NPP ranged from 622.64 to 1323.82 gC·m-2·a-1, with an overall increase rate of 16.15 gC·m-2·a-1; in particular, the contribution of the western forest NPP became increasingly important. Drought stress has limited the growth of subtropical forest NPP in China, with summer and wet season time scales of drought having the greatest impact on forest NPP anomalies, followed by autumn time scales. The limitation is mostly because the drought duration continually increased, leading to differences in the impact of drought on forest NPP before and after 2000, with declines of 59.55 % and 82.45 %, respectively, mainly concentrated in southwestern regions, such as Yunnan, Guangxi, and Sichuan provinces. This study quantitatively analyzed the impact of drought on subtropical forest NPP, and provides scientific basis for subtropical forest response and adaptation to climate change.

Seasonal variations in spatial distribution, mobilization kinetic and toxicity risk of arsenic in sediments of Lake Taihu, China.

This study investigated seasonal variations in spatial distribution, mobilization kinetic and toxicity risk of arsenic (As) in sediments of three representative ecological lakes in Lake Taihu. Results suggested that the bioavailability and mobility of As in sediments depended on the lake ecological types and seasonal changes. At the algal-type zones and macrophyte-type zones, elevated As concentrations were observed in April and July, while these occurred at the transition areas in July and October. The diffusion flux of soluble As ranged from 0.03 to 3.03 ng/cm2/d, indicating sediments acted as a source of As. Reductive dissolution of As-bearing iron/manganese-oxides was the key driver of sediment As remobilization. However, labile S(-II) caused by the degradations of algae and macrophytes buffered sediment As release at the algal-type and macrophyte-type zones. Furthermore, the resupply ratio was less than 1 at three ecological lakes, indicating the resupply As capacity of sediment solid phase was partially sustained case. The risk quotient values were higher than 1 at the algal-type zones and transition areas in July, thereby, the adverse effects of As should not be ignored. This suggested that it is urgently need to be specifically monitored and managed for As contamination in sediments across multi-ecological lakes.

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980-2019.

Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71-0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32-0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment.

Spatiotemporal patterns of rabid dogs and cats and the opinions of personnel responsible for disease control in Thailand.

The aim of this study was to evaluate the patterns of rabies cases in dogs and cats in Thailand from 2013 to 2016 via spatiotemporal analysis. We also assessed the opinions of responsible district-level personnel regarding risk factors and control measures for rabies. Evaluation of rabies case patterns was based on secondary data from a national online database, and a structured questionnaire was used to obtain the opinions of district-level personnel. A total of 1202 cases (1202/13058, 9.21 %) of rabid dogs and cats were documented between 2013 and 2016, with the majority of cases involving dogs (1165/13058; 8.92 %). The spatiotemporal analysis indicated that most of the cases were recorded in central Thailand and that there was a general trend of an increase in rabies cases from the beginning of 2013 to the end of 2016. Month-by-month analysis for each year suggested that the number of rabies cases tended to increase over the course of the year in 2013 and 2016. Results from the autocorrelation indicated that the correlation coefficient tended to be similar in adjacent time lags. In terms of the opinion analysis, only one factor (i.e., the presence of a forest that served as a habitat for carrier animals in the district) was statistically significant (P < 0.05) in the final binary logistic regression model. The results of this study may facilitate planning for effective rabies control in Thailand.

Distribution and trends of mercury in aquatic and terrestrial biota of New York, USA: a synthesis of 50 years of research and monitoring.

Mercury (Hg) inputs have particularly impacted the northeastern United States due to its proximity to anthropogenic emissions sources and abundant habitats that efficiently convert inorganic Hg into methylmercury. Intensive research and monitoring efforts over the past 50 years in New York State, USA, have informed the assessment of the extent and impacts of Hg exposure on fishes and wildlife. By synthesizing Hg data statewide, this study quantified temporal trends of Hg exposure, spatiotemporal patterns of risk, the role that habitat and Hg deposition play in producing spatial patterns of Hg exposure in fish and other wildlife, and the effectiveness of current monitoring approaches in describing Hg trends. Most temporal trends were stable, but we found significant declines in Hg exposure over time in some long-sampled fish. The Adirondack Mountains and Long Island showed the greatest number of aquatic and terrestrial species with elevated Hg concentrations, reflecting an unequal distribution of exposure risk to fauna across the state. Persistent hotspots were detected for aquatic species in central New York and the Adirondack Mountains. Elevated Hg concentrations were associated with open water, forests, and rural, developed habitats for aquatic species, and open water and forested habitats for terrestrial species. Areas of consistently elevated Hg were found in areas driven by atmospheric and local Hg inputs, and habitat played a significant role in translating those inputs into biotic exposure. Continued long-term monitoring will be important in evaluating how these patterns continue to change in the face of changing land cover, climate, and Hg emissions.

Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns.

In this study, we investigate the spatiotemporal dynamics of the neural oscillations by analyzing the electric potential that arises from neural activity. We identify two types of dynamics based on the frequency and phase of oscillations: standing waves or as out-of-phase and modulated waves, which represent a combination of standing and moving waves. To characterize these dynamics, we use optical flow patterns such as sources, sinks, spirals and saddles. We compare analytical and numerical solutions with real EEG data acquired during a picture-naming task. Analytical approximation of standing waves helps us to establish some properties of pattern location and number. Specifically, sources and sinks are mainly located in the same location, while saddles are positioned between them. The number of saddles correlates with the sum of all the other patterns. These properties are confirmed in both the simulated and real EEG data. In particular, source and sink clusters in the EEG data overlap with each other with median percentages around 60%, and hence have high spatial correlation, while source/sink clusters overlap with saddle clusters in less than 1%, and have different locations. Our statistical analysis showed that saddles account for about 45% of all patterns, while the remaining patterns are present in similar proportions.

A global satellite observation of phytoplankton taxonomic groups over the past two decades.

Marine phytoplankton fuel the oceanic biotic chain, determine the carbon sequestration levels, and are crucial for the global carbon cycle and climate change. In the present study, we show a near-two-decadal (2002-2022) spatiotemporal distribution of global phytoplankton abundance, proxy as dominant phytoplankton taxonomic groups (PTGs), with a newly developed remote sensing model. Globally, six chief PTGs, namely chlorophytes (~26%), diatoms (~24%), haptophytes (~15%), cryptophytes (~10%), cyanobacteria (~8%), and dinoflagellates (~3%), explain most of the variation (~86%) in phytoplankton assemblages. Spatially, diatoms generally dominate high latitudes, marginal seas, and coastal upwelling zones, whereas chlorophytes and haptophytes control the open oceans. Satellite observations reveal a gentle multi-annual trend of the PTGs in the major oceans, indicative of roughly "unchanged" conditions on the total biomass or compositions of the phytoplankton community. Jointly, "changed" status applies to a short-term (seasonal) timescale: (1) Fluctuations of PTGs exhibit different amplitudes among different subregions, together with a general rule-more intense vibration in the Northern Hemisphere and polar oceans than other zones; (2) diatoms and haptophytes vary more dramatically than other PTGs in a global-scale scope. These findings provide a clear picture of the global phytoplankton community composition and can improve our understanding of their state and further analysis of marine biological processes.

Climate drivers of seed rain phenology of subtropical forest communities along an elevational gradient.

Seed rain phenology (the start and end date of seed rain) is an essential component of plant phenology, critical for understanding population regeneration and community dynamics. However, intra- and inter-annual changes of seed rain phenology along environmental gradients have rarely been studied and the responses of seed rain phenology to climate variations are unclear. We monitored seed rain phenology of four forest communities in four years at different elevations (900 m, 1450 m, 1650 m, 1900 m a.s.l.) of a subtropical mountain in Central China. We analyzed the spatiotemporal patterns of seed rain phenology of 29 common woody plant species (total observed species in the seed rain), and related the phenological variations to seed number and climatic variables using mixed-effect models with the correlation matrix of phylogeny. We found that changes in the period length were mainly driven by the end rather than the start date. The end date and the period length of seed rain were significantly different between the mast and non-mast seeding years, while no significant elevation-related trend was detected in seed rain phenology variation. Seed number, mean temperature in spring (Tspr), and winter (Twin), summer precipitation (Psum) had significant effects on seed rain phenology. When Tspr increased, the start date of seed rain advanced, while the end date was delayed and the seed rain period length was mainly prolonged by a higher seed number, Twin and Psum. Forest canopy might have a buffering effect on understory climatic conditions, especially in precipitation that lead to difference in seed rain phenology between canopy and shrub species. Our novel evidence of seed rain phenology can improve prediction of community regeneration dynamics in responding to climate changes.

An effective tool for tracking steroids and their metabolites at the watershed level: Combining fugacity modeling and a chemical indicator.

Steroids have attracted concern worldwide because of their potential carcinogenicity and severe adverse effects on aquatic organisms. However, the contamination status of various steroids, particularly their metabolites, at the watershed level remains unknown. This was the first study to employ field investigations to elucidate the spatiotemporal patterns, riverine fluxes, and mass inventories, and conduct a risk assessment of 22 steroids and their metabolites. This study also developed an effective tool for predicting the target steroids and their metabolites in a typical watershed based on the fugacity model combined with a chemical indicator. Thirteen steroids in the river water and seven steroids in sediments were identified with total concentrations of 1.0-76 ng/L and

Spatiotemporal patterns and collective dynamics of bi-layer coupled Izhikevich neural networks with multi-area channels.

The firing behavior and bifurcation of different types of Izhikevich neurons are analyzed firstly through numerical simulation. Then, a bi-layer neural network driven by random boundary is constructed by means of system simulation, in which each layer is a matrix network composed of 200 × 200 Izhikevich neurons, and the bi-layer neural network is connected by multi-area channels. Finally, the emergence and disappearance of spiral wave in matrix neural network are investigated, and the synchronization property of neural network is discussed. Obtained results show that random boundary can induce spiral waves under appropriate conditions, and it is clear that the emergence and disappearance of spiral wave can be observed only when the matrix neural network is constructed by regular spiking Izhikevich neurons, while it cannot be observed in neural networks constructed by other modes such as fast spiking, chattering and intrinsically bursting. Further research shows that the variation of synchronization factor with coupling strength between adjacent neurons shows an inverse bell-like curve in the form of "inverse stochastic resonance", but the variation of synchronization factor with coupling strength of inter-layer channels is a curve that is approximately monotonically decreasing. More importantly, it is found that lower synchronicity is helpful to develop spatiotemporal patterns. These results enable people to further understand the collective dynamics of neural networks under random conditions.

Cropland: Surplus or Deficit? From the Perspective of Meeting People's Grain Requirement.

The quantity and quality of cropland plays an important role in ensuring food security. In order to explore spatiotemporal patterns of the extent to which cropland satisfies people's grain need, we integrate multi-source heterogeneous data to investigate in which era, and in which region, the cultivated land can meet people's food demands. It turns out that in the past 30 years, with the exception of the late 1980s, the amount of cropland could satisfy people's grain needs at the nation scale. However, more than 10 provinces (municipality/autonomous region), mainly located in western China and southeast coastal areas, have been unable to meet the grain needs of local people. We projected the guarantee rate to the late 2020s. Our study concludes that the guarantee rate of cropland is estimated to be higher than 150% in China. Compared to 2019, except Beijing, Tianjin, Liaoning, Jilin, Ningxia, as well as Heilongjiang in the Sustainability scenario, and Shanghai in the Sustainability and the Equality scenarios, the guarantee rate of cultivated land will increase in every province (municipality/autonomous region) in 2030. This study has reference value for the study of China's cultivated land protection system, as well as important significance for China's sustainable development.

Characterization of global fire activity and its spatiotemporal patterns for different land cover types from 2001 to 2020.

Fire is a widespread phenomenon that plays an important role in Earth's ecosystems. This study investigated the global spatiotemporal patterns of burned areas, daytime and nighttime fire counts, and fire radiative power (FRP) from 2001 to 2020. The month with the largest burned area, daytime fire count, and FRP presented a bimodal distribution worldwide, with dual peaks in early spring (April) and summer (July and August), while the month with the largest nighttime fire count and FRP showed a unimodal distribution, with a peak in July. Although the burned area showed decline at the global scale, a significant increase occurred in temperate and boreal forest regions, where nighttime fire occurrence and intensity have consistently increased in recent years. The relationships among burned area, fire count, and FRP were further quantified in 12 typical fire-prone regions. The burned area and fire count exhibited a humped relationship with FRP in most tropical regions, whereas the burned area and fire count constantly increased when the FRP was below approximately 220 MW in temperate and boreal forest regions. Meanwhile, the burned area and FRP generally increased with the fire count in most fire-prone regions, indicating an increased risk of more intense and larger fires as the fire count increased. The spatiotemporal dynamics of burned areas for different land cover types were also explored in this study. The results suggest that the burned areas in forest, grassland, and cropland showed dual peaks in April and from July to September while the burned areas in shrubland, bareland, and wetlands usually peaked in July or August. Significant increases in forest burned area were observed in temperate and boreal forest regions, especially in the western U.S. and Siberia, whereas significant increases in cropland burned area were found in India and northeastern China.