Climate change is causing more frequent and intense heatwaves. Therefore, it is important to understand how heatwaves affect the terrestrial carbon cycle, especially in grasslands, which are especially susceptible to climate extremes. This study assessed the impact of naturally occurring, simultaneous short-term heatwaves on CO2 fluxes in three ecosystems on the Mongolia Plateau: meadow steppe (MDW), typical steppe (TPL), and shrub-grassland (SHB). During three heatwaves, net ecosystem productivity (NEP) was reduced by 86â¯%, 178â¯%, and 172â¯% at MDW, TPL, and SHB, respectively. The changes in ecosystem respiration, gross primary production, evapotranspiration, and water use efficiency were divergent, indicating the mechanisms underlying the observed NEP decreases among the sites. The impact of the heatwave in MDW was mitigated by the high soil water content, which enhanced evapotranspiration and subsequent cooling effects. However, at TPL, insufficient soil water led to combined thermal and drought stress and low resilience. At SHB, the ecosystem's low tolerance to an August heatwave was heavily influenced by species phenology, as it coincided with the key phenological growing phase of plants. The potential key mechanism of divergent NEP response to heatwaves lies in the divergent stability and varying importance of environmental factors, combined with the specific sensitivity of NEP to each factor in ecosystems. Furthermore, our findings suggest that anomalies in soil environment, rather than atmospheric anomalies, are the primary determinants of NEP anomalies during heatwaves. This challenges the conventional understanding of heatwaves as a discrete and ephemeral periods of high air temperatures. Instead, heatwaves should be viewed as chronologically variable, compound, and time-sensitive environmental stressors. The ultimate impact of heatwaves on ecosystems is co-determined by a complex interplay of environmental, biological, and heatwave features.
In a rapidly urbanizing world, heavy air pollution and increasing surface temperature pose significant threats to human health and lives, especially in densely populated cities. In this study, we took an information theory perspective to investigate the causal relationship between diel land surface temperature (LST) and transboundary air pollution (TAP) from 2003 to 2020 in the Bangkok Metropolitan Region (BMR), which includes Bangkok Metropolis and its five adjacent provinces. We found an overall increasing trend of LST over the study region, with the mean daytime LST rising faster than nighttime LST. Evident seasonal variations showed high aerosol optical depth (AOD) loadings during the dry period and low loadings at the beginning of the rainy season. Our study revealed that TAP affected diel surface temperature in Bangkok Metropolis significantly. Causality tests show that air pollutants of two adjacent provinces west of Bangkok, i.e., Nakhon Pathom and Samut Sakhon, have a greater influence on the LST of Bangkok than other provinces. Also, the bidirectional relationship indicates that air pollution has a greater impact on daytime LST than nighttime LST. While LST has an insignificant influence on AOD during the daytime, it influences AOD significantly at night. Our study offers a new approach to understanding the causal impact of TAP and can help policymakers to identify the most relevant locations that cause pollution, leading to appropriate planning and management.
Land use competition between agricultural activities and ground-mounted solar photovoltaic (PV) deployment has increased worldwide attention to hybrid agriculture, and PV systems known as agrivoltaic systems (AVS) in efforts to increase the efficiency of energy and food production and minimize the land use competition. However, little is known about AVS's economic feasibility and environmental tradeoffs. Here we aim to evaluate the techno-economic and environmental impacts of four AVS configurations (full density, half density, mono-axial tracking, and bi-axial tracking) and compare their performance against PV-only systems. We used the life cycle revenue generated from a hectare of land area ($/ha) as a functional unit of our analysis. We found that all AVS configurations outperformed PV-only systems in the economic feasibility assessment, where bi-axial tracking was the best-performing AVS. Further, we developed a case scenario for agricultural farmers to determine the minimum selling price of electricity required for AVS to compete with the economic performance of crop-only farms. We found that the AVS designs require additional incentives (2¢ - 6¢ per kWh of electricity generation) to be as competitive as the crop-only farms. The life cycle environmental assessment demonstrated that the AVS has better environmental performance than PV-only systems, with â¼15-55 % less environmental impacts per functional unit. On average, electricity generation accounts for â¼80 % of AVS environmental impacts, while food production and water demand account for â¼20 %. Additionally, a sensitivity analysis conducted on various uncertain parameters, such as crop yield, water demand, electricity selling price, crop selling prices, discount, and inflation rates, while varying these parameters across broader ranges, indicates that AVS designs become a more economically and environmentally sustainable alternative over PV-only systems in the majority (>66 %) of the data analyzed.
BACKGROUND: Cannabis sativa, a dioecious plant that has been cultivated worldwide for thousands of years, is known for its secondary metabolites, especially cannabinoids, which possess several medicinal effects. In this study, we investigated the autopolyploidization effects on the biosynthesis and accumulation of these metabolites, transcriptomic and metabolomic analyses were performed to explore the gene expression and metabolic variations in industrial hemp autotetraploids and their diploid progenitors. RESULTS: Through these analyses, we obtained 1,663 differentially expressed metabolites and 1,103 differentially expressed genes. Integrative analysis revealed that phenylpropanoid and terpenoid biosynthesis were regulated by polyploidization. No substantial differences were found in the cannabidiol or tetrahydrocannabinol content between tetraploids and diploids. Following polyploidization, some transcription factors, including nine bHLH and eight MYB transcription factors, affected the metabolic biosynthesis as regulators. Additionally, several pivotal catalytic genes, such as flavonol synthase/flavanone 3-hydroxylase, related to the phenylpropanoid metabolic pathway, were identified as being modulated by polyploidization. CONCLUSIONS: This study enhances the overall understanding of the impact of autopolyploidization in C. sativa and the findings may encourage the application of polyploid breeding for increasing the content of important secondary metabolites in industrial hemp.
Cannabis , Transcriptome , Cannabis/genetics , Diploidy , Terpenes , Plant Breeding , Transcription Factors/genetics
Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSKD) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSKD database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000-2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.
Ecosystem , Livestock , Animals , Goats , Horses , Kazakhstan , Sheep
Natural plant nanocrystalline cellulose (NCC), exhibiting a number of exceptional performance characteristics, is widely used in food fields. However, little is known about the relationship between NCC and the antiviral effect in animals. Here, we tested the function of NCC in antiviral methods utilizing honey bees as the model organism employing Israeli acute paralysis virus (IAPV), a typical RNA virus of honey bees. In both the lab and the field, we fed the IAPV-infected bees various doses of jute NCC (JNCC) under carefully controlled conditions. We found that JNCC can reduce IAPV proliferation and improve gut health. The metagenome profiling suggested that IAPV infection significantly decreased the abundance of gut core bacteria, while JNCC therapy considerably increased the abundance of the gut core bacteria Snodgrassella alvi and Lactobacillus Firm-4. Subsequent metabolome analysis further revealed that JNCC promoted the biosynthesis of fatty acids and unsaturated fatty acids, accelerated the purine metabolism, and then increased the expression of antimicrobial peptides (AMPs) and the genes involved in the Wnt and apoptosis signaling pathways against IAPV infection. Our results highlighted that JNCC could be considered as a prospective candidate agent against a viral infection.
Corchorus , Dicistroviridae , Gastrointestinal Microbiome , Bees , Animals , Cellulose/pharmacology , Corchorus/genetics , Antiviral Agents/pharmacology
The contribution of lateral carbon (C) to hydrological processes is well known for its ecological functions in the estuarine C budget across the terrestrial-aquatic interfaces. However, sampling of individual daily tides during multiple months or seasons in heterogeneous patches of landscape makes extrapolation from days to months or seasons challenging. In this paper, we examine the terrestrial-aquatic lateral hydrological C flux for an estuarine marsh where monthly tides, including consecutive daily spring tides, were measured over the course of an entire year. We found a significant correlation between imported and exported hydrological dissolved C, both dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC), although a similar correlation was not found for particulate organic carbon (POC). Based on a total of 44 sampling trips over a year, this saltmarsh appeared to be a net exporter of DOC and DIC but a net sink of POC. Furthermore, the lateral hydrological C budget functioned as a limited lateral C sink in terms of organic C (i.e., ΔPOC and ΔDOC), while the marsh functioned as a small lateral C source. Our findings highlight the importance of lateral hydrologic inflows/outflows in wetland C budgets of land-water interfaces, especially in those characterized by the meta-ecosystem framework. Surprisingly, different C species responded unequally to the lateral hydrological C budget, suggesting that a conceptual realization of meta-ecosystem is a powerful theoretical framework to extend the outwelling hypothesis.
The ratio of carbon assimilation to water evapotranspiration (ET) of an ecosystem, referred to as ecosystem water use efficiency (WUEeco), is widely expected to increase because of the rising atmospheric carbon dioxide concentration (Ca). However, little is known about the interactive effects of rising Ca and climate change on WUEeco. On the basis of upscaled estimates from machine learning methods and global FLUXNET observations, we show that global WUEeco has not risen since 2001 because of the asymmetric effects of an increased vapor pressure deficit (VPD), which depressed photosynthesis and enhanced ET. An undiminished ET trend indicates that rising temperature and VPD may play a more important role in regulating ET than declining stomatal conductance. Projected increases in VPD are predicted to affect the future coupling of the terrestrial carbon and water cycles.
Carbon Dioxide , Climate Change , Ecosystem , Photosynthesis , Vapor Pressure , Water Cycle , Hot Temperature
[This corrects the article DOI: 10.3389/fpls.2022.1023571.].
Livestock-grassland interactions are among the most important relationships in grazed grassland ecosystems, where herbivores play a crucial role in plant community and ecosystem functions. However, previous studies primarily have focused on the responses of grasslands to grazing, with few focussing on the effects of livestock behaviour that in turn would influence livestock intake and primary and secondary productivity. Through a 2-year grazing intensity experiment with cattle in Eurasian steppe ecosystem, global positioning system (GPS) collars were used to monitor animal movements, where animal locations were recorded at 10-min intervals during the growing season. We used a random forest model and the K-means method to classify animal behaviour and quantified the spatiotemporal movements of the animals. Grazing intensity appeared to be the predominant driver for cattle behaviour. Foraging time, distance travelled, and utilization area ratio (UAR) all increased with grazing intensity. The distance travelled was positively correlated with foraging time, yielding a decreased daily liveweight gain (LWG) except at light grazing. Cattle UAR showed a seasonal pattern and reached the maximum value in August. In addition, the canopy height, above-ground biomass, carbon content, crude protein, and energy content of plants all affected cattle behaviour. Grazing intensity and the resulting change in above-ground biomass and forage quality jointly determined the spatiotemporal characteristics of livestock behaviour. Increased grazing intensity limited forage resources and promoted intraspecific competition of livestock, which induced longer travelling distance and foraging time, and more even spatial distribution when seeking habitat, which ultimately led to a reduction in LWG. In contrast, under light grazing where there were sufficient forage resources, livestock exhibited higher LWG with less foraging time, shorter travelling distance, and more specialized habitat occupation. These findings support the Optimal Foraging Theory and the Ideal Free Distribution model, which may have important implications for grassland ecosystem management and sustainability.
Ecosystem , Grassland , Animals , Cattle , Herbivory/physiology , Biomass , Plants , Livestock
In this paper, a dual position-sensitive detector-based vision measurement system camera is built instead of a traditional CCD camera. The 3D position information for the light point is calculated according to the 2D coordinate information of a certain light point in the space illuminated on the two position-sensitive detector (PSD) photosensitive surfaces, which is used for position detection of the spatial light point. In addition, the positioning model for 2D PSDs with different spot sizes in the Gaussian spot mode is derived by the mathematical model of Lucovsky's differential equation for a PSD. For the nonlinear distortion of the PSD, a nonlinear error calibration method using a particle swarm combined with a back propagation neural network is proposed to correct the errors in the measured values through the relationship between the input and output values, to obtain the predicted value that approximates the real coordinates. Then, by comparing the influence of different spot sizes on the positioning accuracy, we conclude that the smaller the spot formed by the convergence of the beam under the optical lens, the higher the positioning accuracy. We believe this conclusion can help improve the accuracy of PSD measurements. Finally, a red LED light spot is set up, and the 3D position measurement and error calibration of the light spot is done by dual PSD cameras, which better solves the position detection problem of a space light spot under close-range conditions because it is fast, reliable, and easy to implement. It also provides an effective method to detect the motion trajectory of a moving light spot in space.
We investigate the spatiotemporal patterns and environmental controls of the end of the vegetation growing season (EOS) in autumn across the alpine and temperate grasslands of China from 2001 through 2020, focusing on whether the EOS is likely a "dryness effect" due to drought or a "coolness effect" caused by cold temperature in autumn. The results show that the EOS date is earlier (â¼6 days earlier on average) in alpine grasslands than in temperate grasslands. During 2001-2020, a slight non-significant delay of 1.0 day/decade is observed for the regional averaged EOS, which is mostly induced by the delayed EOS in 64.4 % of the study region. Preseason temperature (1-2 months before the EOS) exerts a positive control on the EOS in most of the alpine grasslands and some regions of the eastern part of the temperate grasslands, while drought with a mean length of 3.2 months before the EOS exerts positive effects on the EOS in the central, southwestern, and western parts of the temperate grasslands and in the northeastern part of the alpine grasslands. The positive effects of temperature and drought are very likely phenomena reflecting that the EOS is the "coolness effect" associated with lower temperatures in autumn and the "dryness effect" due to drought, especially meteorological drought without consideration of soil moisture, in late summer and/or early autumn, respectively. Our findings are supported by an analysis of the spatial patterns of the cold degree days (CDD) and EOS sensitivity to the CDD. However, the negative effects of drought are also found in eastern temperate grasslands, likely caused by decreased temperature accompanied by increased moisture. The results presented here highlight the importance of incorporating the impacts of droughts on EOS variability, as well as their interactive effects with temperature, into current vegetation autumn phenology models for grasslands.
Droughts , Soil , Seasons , Temperature , China , Climate Change , Ecosystem
Background: Transitional economies in Southeast Asia-a distinct group of developing countries-have experienced rapid urbanization in the past several decades due to the economic transition that fundamentally changed the function of their economies, societies and the environment. Myanmar, one of the least developed transitional economies in Southeast Asia, increased urbanization substantially from 25% in 1990 to 31% in 2019. However, major knowledge gaps exist in understanding the changes in urban land use and land cover and environment and their drivers in its cities. Methods: We studied Yangon, the largest city in Myanmar, for the urbanization, environmental changes, and the underlying driving forces in a radically transitioned economy in the developing world. Based on satellite imagery and historic land use maps, we quantified the expansion of urban built-up land and constructed the land conversion matrix from 1990 through 2020. We also used three air pollutants to illustrate the changes in environmental conditions. We analyzed the coupled dynamics among urbanization, economic development, and environmental changes. Through conducting a workshop with 20 local experts, we further analyzed the influence of human systems and natural systems on Yangon's urbanization and sustainability. Results: The city of Yangon expanded urban built-up land rapidly from 1990 to 2000, slowed down from 2000 to 2010, but gained momentum again from 2010 to 2020, with most newly added urban built-up land appearing to be converted from farmland and green land in both 1990-2000 and 2010-2020. Furthermore, the air pollutant concentration of CO decreased, but that of NO2 and PM2.5 increased in recent years. A positive correlation exists between population and economic development and the concentration of PM2.5 is highly associated with population, the economy, and the number of vehicles. Finally, the expert panel also identified other potential drivers for urbanization, including the extreme climate event of Cyclone Nargis, capital relocation, and globalization. Conclusions: Our research highlights the dramatic expansion of urban land and degradation of urban environment measured by air pollutants and interdependent changes between urbanization, economic development, and environmental changes.
Jute (Corchorus sp.) is the most important bast fiber crop worldwide; however, the mechanisms underlying domestication and improvement remain largely unknown. We performed multi-omics analysis by integrating de novo sequencing, resequencing, and transcriptomic and epigenetic sequencing to clarify the domestication and improvement of dark jute Corchorus olitorius. We demonstrated that dark jute underwent early domestication and a relatively moderate genetic bottleneck during improvement breeding. A genome-wide association study of 11 important agronomic traits identified abundant candidate loci. We characterized the selective sweeps in the two breeding stages of jute, prominently, soil salinity differences played an important role in environmental adaptation during domestication, and the strongly selected genes for improvement had an increased frequency of favorable haplotypes. Furthermore, we speculated that an encoding auxin/indole-3-acetic acid protein COS07g_00652 could enhance the flexibility and strength of the stem to improve fiber yield. Our study not only provides valuable genetic resources for future fiber breeding in jute, but also is of great significance for reviewing the genetic basis of early crop breeding.
Corchorus , Corchorus/genetics , Corchorus/metabolism , Domestication , Genome-Wide Association Study , Plant Breeding , Sequence Analysis, DNA
The replacement of natural lands with urban structures has multiple environmental consequences, yet little is known about the magnitude and extent of albedo-induced warming contributions from urbanization at the global scale in the past and future. Here, we apply an empirical approach to quantify the climate effects of past urbanization and future urbanization projected under different shared socioeconomic pathways (SSPs). We find an albedo-induced warming effect of urbanization for both the past and the projected futures under three illustrative scenarios. The albedo decease from urbanization in 2018 relative to 2001 has yielded a 100-year average annual global warming of 0.00014 [0.00008, 0.00021] °C. Without proper mitigation, future urbanization in 2050 relative to 2018 and that in 2100 relative to 2018 under the intermediate emission scenario (SSP2-4.5) would yield a 100-year average warming effect of 0.00107 [0.00057,0.00179] °C and 0.00152 [0.00078,0.00259] °C, respectively, through altering the Earth's albedo.
Global Warming , Urbanization , Climate , Climate Change
Kazakhstan is part of the Eurasian Steppes, the world's largest contiguous grassland system. Kazakh grassland systems are largely understudied despite being historically important for agropastoral practices. These grasslands are considered vulnerable to anthropogenic activities and climatic variability. Few studies have examined vegetation dynamics in Central Asia owing to the complex impacts of moisture, climatic and anthropogenic forcings. A comprehensive analysis of spatiotemporal changes of vegetation and its driving factors will help elucidate the causes of grassland degradation. Here, we investigated the individual and pairwise interactive influences of various social-environmental system (SES) drivers on greenness dynamics in Kazakhstan. We sought to examine whether there is a relationship between peak season greenness and its drivers - spring drought, preceding winter freeze-thaw cycles, percent snow cover and snow depth - for Kazakhstan during 2000-2016. As hypothesized, snow depth and spring drought accounted for 60 % and 52 % of the variance in the satellite-derived normalized difference vegetation index (NDVI) in Kazakhstan. The freeze-thaw process accounted for 50 % of NDVI variance across the country. In addition, continuous thawing during the winter increased vegetation greenness. We also found that moisture and topographic factors impacted NDVI more significantly than socioeconomic factors. However, the impacts of socioeconomic drivers on vegetation growth were amplified when they interacted with environmental drivers. Terrain slope and soil moisture had the highest q-values or power of determinant, accounting for ~70 % of the variance in NDVI across the country. Socioeconomic drivers, such as crop production (59 %), population density (48 %), and livestock density (26 %), had significant impacts on vegetation dynamics in Kazakhstan. We found that most of the pairwise interactive influences of the drivers exhibited bi-factor enhancement, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of SES drivers and quantified the impacts of various driving factors on NDVI. These findings can help us identify the factors causing grassland degradation and provide a scientific basis for ecological protection in semiarid regions.
Climate Change , Environmental Monitoring , Ecosystem , Kazakhstan , Seasons , Snow , Soil
Vegetation in Northeast China (NEC) has faced dual challenges posed by climate change and human activities. However, the factors dominating vegetation development and their contribution remain unclear. In this study, we conducted a comprehensive evaluation of the response of vegetation in different land cover types, climate regions, and time scales to water availability from 1990 to 2018 based on the relationship between normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). The effects of human activities and climate change on vegetation development were quantitatively evaluated using the residual analysis method. We found that the area percentage with positive correlation between NDVI and SPEI increased with time scales. NDVI of grass, sparse vegetation, rain-fed crop, and built-up land as well as sub-humid and semi-arid areas (drylands) correlated positively with SPEI, and the correlations increased with time scales. The negatively correlated area was concentrated in humid areas or areas covered by forests and shrubs. Vegetation water surplus in humid areas weakens with warming, and vegetation water constraints in drylands enhance. Moreover, potential evapotranspiration had an overall negative effect on vegetation, and precipitation was a controlling factor for vegetation development in semi-arid areas. A total of 53% of the total area in NEC showed a trend of improvement, which is mainly attributed to human activities (93%), especially through the implementation of ecological restoration projects in NEC. The relative role of human activities and climate change in vegetation degradation areas were 56% and 44%, respectively. Our findings highlight that the government should more explicitly consider the spatiotemporal heterogeneity of the influence of human activities and water availability on vegetation under changing climate and improve the resilience of regional water resources. The relative proportions and roles map of climate change and human activities in vegetation change areas provide a basis for government to formulate local-based management policies.
Climate Change , Ecosystem , China , Human Activities , Humans , Temperature , Water
BACKGROUND: Herein, we tried to develop a prognostic prediction model for patients with LUAD based on the expression profiles of lipid metabolism-related genes (LMRGs). METHODS: Molecular subtypes were identified by non-negative matrix factorization (NMF) clustering. The overall survival (OS) predictive gene signature was developed and validated internally and externally based on online data sets. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve, nomogram, restricted mean survival time (EMST), and decision curve analysis (DCA) were used to assess the performance of the gene signature. RESULTS: We identified three molecular subtypes in LUAD with distinct characteristics on immune cells infiltration and clinical outcomes. Moreover, we confirmed a seven-gene signature as an independent prognostic factor for patients with LUAD. Calibration and DCA analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. In addition, the nomogram showed higher robustness and clinical usability compared with four previously reported prognostic gene signatures. CONCLUSIONS: Findings in the present study shed new light on the characteristics of lipid metabolism within LUAD, and the established seven-gene signature can be utilized as a new prognostic marker for predicting survival in patients with LUAD.
