Spatial-temporal Dynamics Research Articles

Designing and modeling an IoT-based software system for land suitability assessment use case

Assessing the quality of land is a very important step that precedes the planning of land use and taking management decisions; for example, in the agricultural field, it can be used to evaluate the suitability of the land for planting crops, determine the suitable irrigation system type, or adjust the agricultural inputs such as fertilizers and pesticides according to the requirements of each zone in the land. The spatial–temporal dynamic nature of land characteristics entails also updated evaluation process and updated management plan. The present paper tries to exploit the advances in information and communication technologies to develop a conceptual design of a dynamic system that accommodates the spatial–temporal dynamics of the agricultural soil characteristics to realize a land suitability assessment (LSA) based on a factor analysis method. The proposed design combines IoT technologies, web development, database, and digital mapping and tries to consolidate the system with other functionalities useful for decision support and suitable for different cases. The paper conducted a survey and made comparisons to select the best technologies that fit the current use case implementation and presents its reproducible conceptual modeling by developing the static and dynamic views through schemas, diagrams, message sequence charts, IoT messaging topic tree, pseudocode, etc. The functionality of the design was validated with a simple implementation of the system model. To our knowledge, there is no previous significant contribution that has addressed a LSA IoT use case. The proposed design automates the LSA process for more accurate decision-making, saving cost, time, and effort consumed in repeated field trips. It is characterized by flexibility and centralization in its offered services of spatial analysis, detection, visualizations, and status monitoring. The design also allows for remote control of field machinery.

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.

Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

Eco-evolutionary evidence for the global diversity pattern of Cycas (Cycadaceae).

The evolution of the latitudinal diversity gradient (LDG), characterized by a peak in diversity toward the tropics, has captured significant attention in evolutionary biology and ecology. However, the inverse LDG (i-LDG) mechanism, wherein species richness increases toward the poles, remains inadequately explored. Cycads are among one of the oldest lineages of extant seed plants and have undergone extensive diversification in the tropics. Intriguingly, the extant cycad abundance exhibits an i-LDG pattern, and the underlying causes for this phenomenon remain largely elusive. Here, using 1,843 nuclear genes from a nearly complete sampling, we conducted comprehensive phylogenomic analyses to establish a robust species-level phylogeny for Cycas, the largest genus within cycads. We then reconstructed the spatial-temporal dynamics and integrated global environmental data to evaluate the roles of species ages, diversification rates, contemporary environment, and conservatism to ancestral niches in shaping the i-LDG pattern. We found Cycas experienced decreased diversification rates, coupled with the cooling temperature since its origin in the Eocene from continental Asia. Different regions have distinctively contributed to the formation of i-LDG for Cycas, with the northern hemisphere acting as evolutionary museums and the southern hemisphere serving as cradles. Moreover, water-related climate variables, specifically precipitation seasonality and potential evapotranspiration, were identified as paramount factors constraining Cycas species richness in the rainforest biome near the equator. Notably, the adherence to ancestral monsoonal climates emerges as a critical factor in sustaining the diversity pattern. This study underscores the imperative of integrating both evolutionary and ecological approaches to comprehensively unravel the mechanisms underpinning global biodiversity patterns.

The landscape altered the interaction between vegetation and climate in the desert oasis of Hotan River Basin, Xinjiang, China

In the arid region, the oasis is characterized by a desert substance and a mosaic of vegetation. The biophysical processes and interactions between vegetation and the local climate in this kind of region are determined by the macroscopic structure of the ecosystems, i.e. landscape patterns. To understand how these landscape patterns impact regional hydro-heat coupling across space and evolve over time, we utilized remote sensing observational data and methods to examine the relationships among these factors. In this case, we focused on the oasis along the upstream of the Hotan River Basin in the Taklamakan Desert in Xinjiang of western China and employed the satellite imagery datasets of Landsat from 1993 to 2019 to investigate the dynamics of vegetation–climate factors. Based on the land use and cover change datasets, the landscape pattern metrics, including patch density (PD), contagion index (CONTAG), fractal dimension (FRAC), were calculated to measure the landscape features on the different aspects, i.e. the fragmentation, the connectivity, and the complexity. With the algorithm of land surface energy balance, the land surface indicators, including the soil-adjusted vegetation index (SAVI), albedo, surface irradiance temperature (Ts), and evapotranspiration (ET), were calculated to represent the key process in the interaction of vegetation–climate. The temporal-spatial dynamics of the landscape patterns and the vegetation–climate metrics were mapped and demonstrated in a quantitative manner. The statistical results revealed that during the past decades, the agricultural land in the study area had significantly increased by 17 %. Grassland and shrubs had also expanded, while the desert area decreased from 82.57 % to 78.82 % of the total area, with an overall reduction rate of 1.4 %/10a. It was found the study area was getting warmer and dryer based on the general trends of Ts and ET observed during the period of 1993–2019. The agricultural land had the highest PD and CONTAG, and the lowest FRAC. The agricultural land had the lowest Ts and the highest ET. The results of structural equation model identified the decoupling effects of PD and CONTAG on the regional hydro-heat environment, while confirmed that FRAC had positive impact on the coupling between Ts and ET. Our study bridged the landscape pattern with the regional vegetation–climate interaction and provided the suggestions for the landscape planning and management for a more sustainable arid region.

Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions.

Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels-system, cell, and environment-within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.

Current trends and spatial-temporal dynamics of veterinary dentistry research: A scientometric study.

Understanding dental care in dogs has made remarkable progress in veterinary medicine. Therefore, this study aimed to analyze the academic literature published in veterinary dentistry from 1990 to 2023. A descriptive study was conducted using a scientometric approach and metadata from the Web of Science database. A search strategy adapted for this database was developed using MeSH and Emtree terms and the Boolean operators AND and OR. Using Bibliometrix, different metrics were evaluated to assess the scientific production of researchers and institutions and the impact of authors based on their publications. CiteSpace was also used for co-citation analysis and visualization of citation networks, trends, and patterns in this field of study over time. The bibliometric study analyzed 211 documents from 50 different sources from 1990 to 2023, with an annual growth rate of 6.5%, covering the period 1990-2023. A total of 474 authors were identified, with an average of 2.82 coauthors per paper and 11.85% international coauthorships. The average age of the papers was 12.4 years and 4.55 citations per paper. The most common types of documents were articles (154 documents). Research in veterinary dentistry has shown steady growth from 1990 to 2023. Although there have been fluctuations in article production over the years, there has been a steady growth in article production in veterinary dentistry in general. The annual average number of citations per article has varied over the years, reaching 45 in 2015. However, the average number of citations per article has decreased significantly from 2021 to 2023.

Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction

Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially, a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.

Spatial-Temporal Dynamics of Carbon Budgets and Carbon Balance Zoning: A Case Study of the Middle Reaches of the Yangtze River Urban Agglomerations, China

Analysis of the spatial variation characteristics of regional carbon sources/sinks is a prerequisite for clarifying the position of carbon balance zones and formulating measures to reduce emissions and increase sinks. Studies of carbon sinks have often used the coefficient method, which is limited by sample size, measurement error, and low spatial resolution. In this study, 31 cities in the middle reaches of the Yangtze River urban agglomerations (MRYRUA) were studied with the improved CASA (Carnegie Ames Stanford Approach) model to estimate the grid-scale net ecosystem productivity (NEP) and explore the spatial-temporal evolution of carbon budgets from 2005 to 2020. By calculating the carbon balance index (CBI), economic contribution coefficient (ECC), and ecological support coefficient (ESC), carbon balance zoning was conducted. Corresponding suggestions are based on the carbon balance zoning results. From 2005 to 2020, carbon budgets increased and were high in the north-central region and low in the south. In addition, carbon sink functional zones were distributed in cities with rich ecological resources. Low-carbon economic zones shifted from the Poyang Lake Urban Agglomeration to the Wuhan City Circle; low-carbon optimization zones occurred from the Wuhan City Circle to the Poyang Lake Urban Agglomeration. Carbon intensity control and high-carbon optimization zones were distributed in cities with rapid economic development. Our results support the MRYRUA in achieving “double carbon” targets and formulating regional collaborative emissions reduction policies.

Potentially toxigenic phytoplankton patterns in the northwestern Iberian Peninsula

The Galician estuaries are Europe’s foremost supplier of mussels, generating millions of euros annually and offering substantial employment opportunities for its population. One of the most critical threats to shellfish production is the occurrence of harmful algal blooms (HABs), contaminating bivalves with phytotoxins. To successfully tackle this problem, there needs to be a collaborative effort between the scientific community and decision-makers to establish a dynamic and effective monitoring system. This could enable early warnings and preventive actions to avert the loss of millions of tons of shellfish. Remote sensing, despite its limitations, requires commitment and effort by experts to devise effective methods for detecting target optical constituents mixed with other undesired target but that exhibit strong signals. Therefore, the essential necessity arises to identify approaches for mitigating the shortcomings of the undertaken efforts. The objective of this study is to assess the main environmental drivers of potential harmful genera (Pseudo-nitzschia, Dinophysis, Alexandrium, and Gymnodinium) in the Rías Baixas from 2015 to 2022, developing regression-based models and customizing Generalized Additive Models (GAMs) to investigate their spatial-temporal dynamics. Risk-susceptible bloom zones were identified in the river mouth of the Ría Pontevedra and from the center to offshore of the Ría Vigo. Early upwelling events triggered peaks in chlorophyll-a (Chl-a), driving Dinophysis grazing on phytoplankton communities mainly dominated by diatoms, flagellates, and ciliates. Subsequently, the upwelling intensity favoring Pseudo-nitzschia or Alexandrium minutum growth as larger diatoms declined. A. minutum exhibited elevated incidence over the past five years. Gymnodinium catenatum presented a rare occurrence across the three studied estuaries, which limited the assessment of its spatial dynamics in the region. This study emphasizes the need to integrate remote sensing evaluation of high-risk bloom areas (July to September), in-situ cell count collection, and enhanced efforts for forecasting future critical occurrences of HABs.

Unveiling China’s natural and planted forest spatial–temporal dynamics from 1990 to 2020

Understanding the spatial–temporal dynamics of both natural and planted forests is critical for sustainable forest management and assessing ecological benefits or impacts. In 2020, China’s forest area encompassed approximately 220 million hectares, accounting for around 5 % of the global forest area. Furthermore, China boasts the largest planted forest area worldwide. However, knowledge regarding the spatial–temporal dynamics of China’s natural and planted forests has remained elusive. To fill this gap, we first generated wall-to-wall natural and planted forest maps at 30-m resolution for China every-five years from 1990 to 2020 by using 447,730 images from Landsat-4/5/7/8/9 surface reflectance archive and 656,920 field samples, which were a combination of crowdsourced data and expert knowledge. Secondly, we analyzed the spatial–temporal dynamics of natural and planted forests at multiple-scales from 1990 to 2020. The resultant maps achieved an overall accuracy ranging from 77.33 %±0.67 % to 81.78 % ± 0.59 % and revealed opposite trends in the areas of natural and planted forests from 1990 to 2020. During these three decades, China’s planted forest area increased by 447,500 km2 while the natural forest area decreased by 219,100 km2. The spatial–temporal dynamics of China’s natural and planted forests result from China’s vast forest management programs in addition to social and economic factors. Variations of these factors at different scales not only determined the primary goals of protecting natural forests and expand planted forests but also resulted in increasing forest areas for many consecutive years by planting forests that offset the loss of natural forests. Our maps provide timely and valuable insights into the benefits and impacts of natural and planted forests in China, which are both closely linked to sustaining human well-being.

Effects of decades of physical driving experience on pre-exposure postural precursors of motion sickness among virtual passengers

Previous research has shown that motion sickness associated with virtual vehicles is more common among passengers than among drivers. Separately, other studies have shown that postural precursors of motion sickness during virtual driving differ as a function of prior experience driving physical vehicles. We investigated the intersection of those prior effects: We asked whether decades of physical driving experience 1) would influence motion sickness among passengers in a virtual vehicle, and 2) would influence postural precursors of motion sickness among passengers in a virtual vehicle. In our study, middle-aged adults were exposed to a virtual vehicle as passengers. Some participants (Physical Drivers) had decades of experience driving physical automobiles, while others (Physical Non-Drivers) had rarely or never driven a physical vehicle. First, we measured head and torso movement as standing participants performed simple visual tasks. Then, each participant watched a recording of the motion of a virtual vehicle, which induced motion sickness in some participants. Afterward, neither the incidence nor the severity of motion sickness differed between Physical Drivers and Physical Non-Drivers. Our analysis of pre-exposure standing body sway revealed postural precursors of motion sickness in measures of the spatial magnitude and temporal dynamics of movement. In statistically significant interactions, these precursors (Well vs. Sick) differed as a function of physical driving experience (Physical Drivers vs. Physical Non-Drivers). Overall, our results indicate that, among virtual passengers, long-term real-world driving experience influenced the postural precursors of motion sickness, but not the incidence or severity of motion sickness. We discuss these results in terms of relationships between perception and motor control in theories of motion sickness etiology.

Understanding the impact of population dynamics on water use utilizing multi-source big data

Abstract Population movement, such as commuting, can affect water supply pressure and efficiency in modern cities. However, there is a gap in the research concerning the relationship between water use and population mobility, which is of great significance for urban sustainable development. In this study, we analyzed the spatial–temporal dynamics of the population and its underlying mechanisms, using multi-source geospatial big data, including Baidu heat maps (BHMs), land use parcels, and point of interest. Combined with water consumption, sewage volume, and river depth data, the impact of population dynamics on water use was investigated. The results showed that there were obvious differences in population dynamics between weekdays and weekends with a ratio of 1.11 for the total population. Spatially, the population concentration was mainly observed in areas associated with enterprises, industries, shopping, and leisure activities during the daytime, while at nighttime, it primarily centered around residential areas. Moreover, the population showed a significant impact on water use, resulting in co-periods of 24 h and 7 days, and the water consumption as well as the wastewater production were observed to be proportional to the population density. This study can offer valuable implications for urban water resource allocation strategies.

A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility

Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow. Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial–temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder–decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial–temporal dependencies and accurately predict traffic flow. We evaluate the proposed approach on multiple benchmark datasets, and the experimental results demonstrate its superior performance compared to state-of-the-art approaches in terms of various metrics.

Mapping the neural dynamics of locomotion across the Drosophila brain

Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.

Spatial-Temporal Dynamics of Urban Green Spaces in Response to Rapid Urbanization and Urban Expansion in Tunis between 2000 and 2020

Over the past two decades, the rate of urbanization has increased significantly worldwide, with more than half of the population already living in cities; this trend continues in numerous countries and regions. Tunisia is a North African country with a rich history and diverse cultural heritage. In Greater Tunis, its capital city, urbanization has accelerated since 1960. Rapid urbanization has increased the demand for grey infrastructure and led to changes in land-use patterns and the destruction of the environment. This study aims to understand and depict the relationship between urban expansion and the green infrastructure in the Greater Tunis area. This study uses land-use data, administrative boundaries vector data, and Google satellite imagery datasets to calculate and analyze the changes in the land use transfer matrix and landscape pattern index of built-up land and green spaces in the Tunisian capital for three periods: 2000, 2010, and 2020. We found that the expansion of built-up areas in Tunis has increased from 8.8% in 2000 to 12.1% in 2020, and changes in green spaces have decreased from 23% in 2000 to 20.9% in 2020. Without planning guidelines, the layout of green spaces has become more fragmented and disorganized. For this reason, we provide programs and suggestions for building a complete ecological network of green spaces in order to provide references and lessons for related studies and cities facing the same problems.

Unraveling the impacts of multiscale landscape patterns and socioeconomic development on water quality: A case study of the National Sustainable Development Agenda Innovation Demonstration Zone in Lincang City, Southwest China

Study regionNational Sustainable Development Agenda Innovation Demonstration Zone in Lincang City, Southwest China Study focusRevealing the current water quality status in rivers and reservoirs and its drivers is crucial to achieving the United Nations Sustainable Development Goal 6 (SDG 6). We assessed the spatial-temporal dynamics and influence of natural and socioeconomic factors on the water quality index (WQI) and water quality parameters of rivers and reservoirs using redundancy analysis (RDA) and the partial least squares path model (PLSPM). New hydrological insights for the regionThe results indicate the following. (1) The annual average values of the WQI in Lincang City from 2018 to 2020 were 92.26, 92.06, and 92.45, respectively. The WQI values in spring, summer, autumn, and winter were 92.48, 90.38, 92.68, and 93.49, respectively. The annual and seasonal water quality levels were good or higher. However, spatial heterogeneity exists for some water quality parameters in Lincang City. (2) The influence of natural and socioeconomic factors on the WQI and water quality parameters is highly complex. The landscape composition, landscape configuration, and pollutant discharges are key factors affecting the water quality of rivers and reservoirs annually and seasonally, and a spatial scale dependence is observed. (3) Chemical and physical factors directly affect the WQI, particularly at small scales (100 m and 500 m buffer zones). Chemical factors have a strong inhibitory effect on the WQI of reservoirs (−0.79, −0.78) and rivers (−0.56, −0.72), whereas physical factors have a weak promoting effect (0.05–0.13). Landscape composition and landscape configuration and indirectly affect the WQI. In contrast, socioeconomic factors and pollution discharge indirectly impact the WQI of rivers and reservoirs through their influence on chemical and physical factors. These findings demonstrate that natural and social factors interact in multiple ways, impacting the water quality of rivers and reservoirs. This interaction depends on the spatial scale. Therefore, it is crucial to consider the appropriate distance from rivers and reservoirs in future land use planning, particularly in the landscape design and planning in Lincang City. Moreover, controlling wastewater discharge from industrial and agricultural activities and domestic water usage is vital for ensuring high water quality.

Rich diversity and active spatial-temporal dynamics of Thalassiosira species revealed by time-series metabarcoding analysis.

Thalassiosira is a species-rich genus in Bacillariophyta that not only contributes positively as primary producer, but also poses negative impacts on ecosystems by causing harmful algal blooms. Although taxonomical studies have identified a large number of Thalassiosira species, however, the composition of Thalassiosira species and their geographical distribution in marine ecosystems were not well understood due primarily to the lack of resolution of morphology-based approaches used previously in ecological expeditions. In this study, we systematically analyzed the composition and spatial-temporal dynamic distributions of Thalassiosira in the model marine ecosystem Jiaozhou Bay by applying metabarcoding analysis. Through analyzing samples collected monthly from 12 sampling sites, 14 Thalassiosira species were identified, including five species that were not previously reported in Jiaozhou Bay, demonstrating the resolution and effectiveness of metabarcoding analysis in ecological research. Many Thalassiosira species showed prominent temporal preferences in Jiaozhou Bay, with some displaying spring-winter preference represented by Thalassiosira tenera, while others displaying summer-autumn preference represented by Thalassiosira lundiana and Thalassiosira minuscula, indicating that the temperature is an important driving factor in the temporal dynamics. The application of metabarcoding analysis, equipped with appropriate molecular markers with high resolution and high specificity and databases of reference molecular marker sequences for potential all Thalassiosira species, will revolutionize ecological research of Thalassiosira species in Jiaozhou Bay and other marine ecosystems.

Biotic interactions between benthic infauna and aerobic methanotrophs mediate methane fluxes from coastal sediments.

Coastal ecosystems dominate oceanic methane (CH4) emissions. However, there is limited knowledge about how biotic interactions between infauna and aerobic methanotrophs (i.e. CH4 oxidizing bacteria) drive the spatial-temporal dynamics of these emissions. Here, we investigated the role of meio- and macrofauna in mediating CH4 sediment-water fluxes and aerobic methanotrophic activity that can oxidize significant portions of CH4. We show that macrofauna increases CH4 fluxes by enhancing vertical solute transport through bioturbation, but this effect is somewhat offset by high meiofauna abundance. The increase in CH4 flux reduces CH4 pore-water availability, resulting in lower abundance and activity of aerobic methanotrophs, an effect that counterbalances the potential stimulation of these bacteria by higher oxygen flux to the sediment via bioturbation. These findings indicate that a larger than previously thought portion of CH4 emissions from coastal ecosystems is due to faunal activity and multiple complex interactions with methanotrophs.

Big data-driven spatio-temporal heterogeneity analysis of Beijing's catering service industry during the COVID-19 pandemic

The Catering Service Industry (CSI) experienced profound impacts due to the COVID-19 pandemic. However, the long-term and multi-timepoint analysis using big data remained limited, influencing governmental decision-making. We applied Kernel Density Estimation, Shannon Diversity Index, and the Geographic detector to explore the spatial heterogeneity and determinants of the CSI in Beijing during the pandemic, with monthly granularity. The temporal-spatial dynamics of the CSI presented a "W"-shaped trend from 2018 to 2023, with pivotal shifts aligning with key pandemic stages. Spatial characteristics exhibited heterogeneity, with greater stability in the city center and more pronounced shifts in peripheral urban zones. Districts facing intricate outbreaks showed lower catering income, and Chinese eateries exhibited heightened resilience compared to others. The CSI displayed strong interconnections with living service sectors. Development in each district was influenced by economic level, population distribution, service facilities convenience, and the risk of the COVID-19 pandemic. Dominant factors included total retail sales of consumer goods, permanent population, average Baidu Heat Index, density of transportation and catering service facilities, infection cases and the consecutive days with confirmed cases existing. Consequently, we suggested seizing post-pandemic recovery as an avenue to unlock the CSI's substantial potential, ushering a fresh phase of growth.