Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Multi-machine learning methods to predict spatial variation characteristics of total nitrogen at watershed scale: Evidences from the largest watershed (Yangtze River Watershed), Asian.

Nitrogen pollution has emerged as a significant threat to the health of global river systems, garnering considerable attention. However, numerous challenges persist in understanding the characteristics and predicting the spatial changes of total nitrogen (TN) at the catchment scale. We leveraged data from 530 monitoring sections to calculate a land-use composite index and perform statistical analyses to explore the primary factors influencing nitrogen enrichment in the Yangtze River Watershed. We developed three machine learning models to forecast future TN concentrations at monitoring points. Our results showed that agricultural activities and rainfall were the primary drivers of monthly variations in TN concentrations. The upstream region of the watershed exhibited larger variations in TN concentrations (0.097 to 11.099 mg/L), significantly higher than the middle and downstream areas (0.348 to 6.844 mg/L). Microbial-mediated organic matter decomposition in sediment and changes in land-use were identified as key contributors to regional differences in nitrogen enrichment. Potential nitrogen sources include sediment release, urban sewage, and agricultural fertilization. Random Forest model achieved a prediction accuracy of 77.6 %, surpassing the BP and LSTM models. We identified 37 high-risk areas of nitrogen enrichment, concentrated in the Chengdu-Chongqing, Yunnan-Central urban cluster, and the Chaohu Lake sub-watershed. Increased urban land-use and industrial inputs primarily influenced nitrogen enrichment in the upstream area, while agricultural inputs were the main drivers in the middle and downstream regions. Our multi-machine learning models identified the relationship between TN and influencing factors, providing a reliable method for assessing nitrogen enrichment risk in the watershed.

An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth.

Introduction: As an exceptional geographical entity, the vegetation of the Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in the south-eastern sector of the QTP, serving as a transition area from sub-tropical evergreen broadleaf forest to high-mountain vegetation. However, there has been limited exploration into predicting the temporal and spatial variability of vegetation cover using anti-interference methods to address outliers in long-term historical data. Additionally, the correlation between these variables and environmental factors in natural forests with complex terrain has rarely been analyzed. Methods: This study has developed an advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional vegetation cover) to accurately evaluate and predict the time and spatial shifts in FVC within the BNNR, utilizing the GEE (Google Earth Engine). The satellite data utilized in this paper consisted of Landsat images spanning from 1986 to2020. By integrating TS and MK methodologies to monitor and assess the FVC trend, the Hurst index was employed to forecast FVC. Furthermore, the association between FVC and topographic factors was evaluated, the partial correlation between FVC and climatic influences was analyzed at the pixel level (30×30m). Results and discussion: Here are the results of this research: (1) Overall, the FVC of the BNNR exhibits a growth trend, with the mean FVC value increasing from 59.40% in 1986 to 68.67% in 2020. (2) The results based on the TS-MK algorithm showed that the percentage of the area of the study area with an increasing and decreasing trend was 59.03% (significant increase of 28.04%) and 22.13% (significant decrease of 6.42%), respectively. The coupling of the Hurst exponent with the Theil-Sen slope estimator suggests that the majority of regions within the BNNR are projected to sustain an upward trend in FVC in the future. (3) Overlaying the outcomes of TS-MK with the terrain factors revealed that the FVC changes were notably influenced by elevation. The partial correlation analysis between climate factors and vegetation changes indicated that temperature exerts a significant influence on vegetation cover, demonstrating a high spatial correlation.

Spatial and temporal evolution characteristics and factors of heat vulnerability in the Pearl River Delta urban agglomeration from 2001 to 2022.

To explore the spatiotemporal evolution characteristics of heat vulnerability in the Pearl River Delta urban agglomeration during heatwave disasters, this research employs the Entropy Weight Method (EWM) to calculate the heat vulnerability assessment results for nine cities in the region spanning from 2001 to 2022. Through the application of kernel density estimation, Moran's I, and the Geographically and Temporally Weighted Regression (GTWR) model, which is proven to be superior to traditional model such as OLS, this study analyzes the dynamic distribution patterns of heat vulnerability in the study area and dissect the trends of influencing factors. The results reveal that from 2001 to 2022, the overall heat vulnerability index in the study area demonstrates a fluctuating downward trend. Key contributors to heat vulnerability include high-frequency and long-duration heatwaves, population sensitivity, and changes in residents' consumption levels. Throughout this period of development, the disparity in heat vulnerability among cities has gradually widened, indicating an overall pattern of uneven development in the region. Future attention should be focused on formulating heat adaptation strategies in areas with high vulnerability to enhance the overall sustainability of the study area.

Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD-GAT-BiLSTM.

The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD-GAT-BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM2.5 data into a series of relatively stable sub-sequences, thus reducing the influence of unknown factors on model prediction capabilities. For each sub-sequence, a GAT is then designed to explore deep spatial relationships among different monitoring stations. Next, a BiLSTM is utilized to learn the temporal features of each decomposed sub-sequence. Finally, the forecasting results of each decomposed sub-sequence are aggregated and summed as the final air quality prediction results. Experiment results on the collected Beijing air quality dataset show that the proposed model presents superior performance to other used methods on both short-term and long-term air quality forecasting tasks.

Spatio-temporal analysis and prediction of land use land cover (LULC) change in Wular Lake, Jammu and Kashmir, India.

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks.

Limited Impacts of Water Diversion on Micro-eukaryotic Community along the Eastern Route of China's South-to-North Water Diversion Project.

The Eastern Route of the South-to-North Water Diversion Project (ER-SNWDP) represents a crucial initiative aimed at alleviating water scarcity in China's northern region. Understanding the dynamics governing the composition and assembly processes of micro-eukaryotic communities within the canal during different water diversion periods holds paramount significance for the effective management of the ER-SNWDP. Our study systematically tracks the dynamics of the micro-eukaryotic community and its assembly processes along the 1045.4 km of canals and four impounded lakes, totaling 3455 km2, constituting the ER-SNWDP during a complete water diversion cycle, utilizing high-throughput sequencing, bioinformatics tools, and null modeling algorithms. The primary objectives of this study are to elucidate the spatial-temporal succession of micro-eukaryotic communities as the water diversion progresses, to delineate the relative importance of deterministic and stochastic processes in community assembly, and to identify the pivotal factors driving changes in micro-eukaryotic communities. Our findings indicate notable variations in the composition and diversity of micro-eukaryotic communities within the ER-SNWDP across different water diversion periods and geographic locations (P < 0.05). This variation is influenced by a confluence of temporal and environmental factors, with limited impacts from water diversion. In essence, the assembly of micro-eukaryotic communities within the ER-SNWDP primarily stemmed from heterogeneous selection driven by deterministic processes. Water diversion exhibited a tendency to decrease community beta diversity while augmenting the influence of stochastic processes in community assembly, albeit this effect attenuated over time. Furthermore, our analysis identified several pivotal environmental parameters, notably including nitrite-nitrogen, nitrate-nitrogen, orthophosphate, and water temperature, as exerting significant effects on micro-eukaryotic communities across different water diversion periods. Collectively, our study furnishes the inaugural comprehensive exploration of the dynamics, assembly processes, and influencing factors governing micro-eukaryotic communities within the ER-SNWDP, thus furnishing indispensable insights to inform the water quality management of this important project.

A bright red fluorescent genetically encoded sensor for lactate imaging.

Lactate plays a crucial role in energy metabolism and greatly impacts protein activities, exerting diverse physiological and pathological effects. Therefore, convenient lactate assays for tracking spatiotemporal dynamics in living cells are desirable. In this paper, we engineered and optimized a red fluorescent protein sensor for l-lactate named FiLa-Red. This indicator exhibited a maximal fluorescence change of 730 % and an apparent dissociation constant (Kd) of approximately 460 µM. By utilizing FiLa-Red and other sensors, we monitored energy metabolism in a multiplex manner by simultaneously tracking lactate and NAD+/NADH abundance in the cytoplasm, nucleus, and mitochondria. The FiLa-Red sensor is expected to be a useful tool for performing metabolic analysis in vitro, in living cells and in vivo.

A novel longitudinal connectivity index to evaluate reticular river networks based on the combination of network maximum flow and resistance distance.

Reticular river networks, essential for ecosystems and hydrology, pose challenges in assessing longitudinal connectivity due to complex multi-path structures and variable flows, exacerbated by human-made infrastructures like sluices. Existing tools inadequately track water flow's spatiotemporal changes, highlighting the need for targeted methods to gauge connectivity within complex river network systems. The Hydraulic Capacity Connectivity Index (HCCI) was developed adopting complex network theory. This involves river networks mapping, nodes and edges construstion, weight factor definition, maximum flow and resistance distance calculation. The connectivity between nodes is represented by the product of the maximum flow and the inverse of the resistance distance. The mean connectivity of each node with all other nodes, denoted as the node connectivity capacity Ci, and the HCCI of the whole river network is defined as the mean of the Ci for all nodes. The HCCI was firstly applied to a symmetrical virtual river network to investigate the factors influencing the HCCI. The results revealed that Ci showed a radial decreasing pattern from the obstructed river reach outwards, and the boundary rivers play the most significant role in regulating the flow dynamics. Subsequently, the HCCI was applied to a real river network in the Yandu district, followed by spatiotemporal statistical analysis comparing with 1D hydraulic model's simulated river discharge. Results showed a high correlation (Pearson coefficient of 0.89) between the HCCI and monthly average river discharge at the global scale. At the local scale, the geographically weighted regression model demonstrated the strong explanatory power of Ci in predicting the distribution of river reach discharge. This suggests that the HCCI addresses multi-path connectivity assessment challenge in reticular river networks, precisely characterizing spatiotemporal flow dynamics. Furthermore, since HCCI is based on a complex network model that can calculate the connectivity between all river node pairs, it is theoretically applicable to other types of river networks, such as dendritic river networks. By identifying low-connectivity areas, HCCI can guide managers in developing scientifically sound and effective strategies for restoring river network hydrodynamics. This can help prevent water stagnation and degradation of water quality, which is beneficial for environmental protection and water resource management.

[Medicinal plant biodiversity in Jilin province based on big data of the fourth national survey of traditional Chinese medicine resources].

Using Origin2022Pro, PAST4.09, GraphPad, and ArcGIS, this study analyzed the big data of the fourth national survey of traditional Chinese medicine resources in Jilin province from five dimensions: differences in resource quantity, taxonomic group, family, and genus, regional distribution, and spatiotemporal distribution, aiming to fully elucidate the biodiversity of medicinal plants in Jilin province. The results indicated that 2 241 species of medicinal plants existed in Jilin province, belonging to 881 genera of 243 families, with 20 dominant families and 3 dominant genera. There were 1 901 species of medicinal plants(belonging to 778 genera of 227 families) in the eastern mountainous region, 1 503 species(belonging to 690 genera of 225 families) in the mid-mountainous areas of the central mountainous region, and 811 species(belonging to 436 genera of 136 families) in the western plain region. The biodiversity of medicinal plants in Jilin province was high and presented a trend of high in the east and low in the west. The medicinal plant resources were mainly concentrated in the eastern mountainous region, and the number of medicinal plant groups had significant diffe-rences between regions, following the trend of western region > central region > eastern region. The species richness was in the order of eastern region > western region > central region. The species diversity structure in the central region was similar to that in the eastern and western regions, while it was significantly different between the western and eastern regions. Compared with the third national survey of traditional Chinese medicine resources, the fourth survey showed an increase of 1 417 species, a decrease of 580 species, and 824 common species, indicating significant changes in the biodiversity of medicinal plants in Jilin province. The reasons for these changes need to be further explored. This article elucidates the background and biodiversity changes of medicinal plant resources in Jilin province, laying a foundation for the protection, utilization, and industrial development of traditional Chinese medicine resources in Jilin province.

Impact of Flooding and Drought Risks on the Cost of Bond Financing for Water Utilities.

The global water cycle has experienced significant changes due to the interplay of climate shifts and human activities, resulting in more frequent and severe droughts and floods. These shifts have started to impact the operational efficiency of water treatment and delivery systems. This, in turn, has implications for the economic performance of these assets and the climate-impacted cost of their financing through the issuing of municipal bonds. Analyzing a decade of water bond data (2009-2019), this study offers empirical evidence for the impact of flood and drought risks on bond investor demand to offset water risks. The results reveal that bond markets factored in coastal flood risks between 2013 and 2019, adjusting by 3-6 basis points (bps) per risk score unit, and riverine flood risks from 2009 to 2013, with a 5-11 basis points increase per risk score unit. These effects were primarily driven by bonds issued in the Pacific Coast and Great Plains regions, respectively. In contrast, the pricing of drought risks in the bond market followed a more nuanced pattern. Additionally, we show the channeling effects of water consumption and investor perceptions of climate change on water risk pricing in the bond market. These findings have significant implications for water risk management in the public sector as regions with heightened water risk exposure are perceived as riskier by market participants, leading to a higher cost of capital for municipalities and water agencies.

A flexible consistent framework for modelling multiple interacting environmental responses to management in space and time.

Management of resources is often a large-scale task addressed using many small-scale interventions. The range of scales at which organisms respond to those interventions, along with the many outcomes which management aims to achieve can make determining the success of management complex. Environmental flow is an example of management where there is a recognized need for managers to demonstrate the impact of their actions by integrating different types of environmental responses. Here, we aim to support decision making in environmental management via the development of a new modelling framework (eFlowEval). It has the capacity to capture best-available knowledge, to scale it in space and time, explore interactions among species, compare scenarios, and account for uncertainty. Thus, it provides a basis for including multiple target groups in a common system. The framework is readily updatable as new information becomes available and can identify where data are insufficient to be scientifically robust. We demonstrate the eFlowEval framework using three very different environmental responses: 1) metabolism, which is a measure of the energy produced and then used in an ecosystem, 2) favorability for a bird species of interest (royal spoonbill Platalea regia), and 3) competing wetland plants (Centipeda cunninghamii and lippia Phyla canescens). These demonstrations illustrate the capability of the eFlowEval framework but the specific outputs shown here should not be used to assess environmental responses to management. Using these demonstrations, we illustrate the capacity of the eFlowEval framework to provide assessments across a range of scales (local to landscape) and from short time frames (weeks to months) to multi-year assessments. Further, we illustrate the ability to: i) scale responses from local to basin scales, ii) vary driver-response model types, iii) represent uncertainty, iv) compare scenarios, v) accommodate variable parameter values at different locations, and vi) incorporate spatial and temporal dependencies and dependencies among species. We also illustrate the framework's ability to capture inter- and intraspecific interactions and their impact in space and time. The eFlowEval framework extends the capacity of the component response models to provide novel modeling capabilities for management at scale. It allows for interactions among species or processes to be incorporated, as well as in space and time. A large degree of flexibility is offered by the framework, in terms of driver-response model types, input data, and aggregation methods. Thus, the eFlowEval framework provides a mechanism to enhance the transparency of environmental watering decision making, capture institutional knowledge, enhance adaptive management and undertake evaluation of the impact of environmental watering at a range of spatial and temporal scales.

Engineering a Sinorhizobium meliloti Chassis with Monopartite, Single Replicon Genome Configuration.

Multipartite bacterial genomes pose challenges for genome engineering and the establishment of additional replicons. We simplified the tripartite genome structure (3.65 Mbp chromosome, 1.35 Mbp megaplasmid pSymA, 1.68 Mbp chromid pSymB) of the nitrogen-fixing plant symbiont Sinorhizobium meliloti. Strains with bi- and monopartite genome configurations were generated by targeted replicon fusions. Our design preserved key genomic features such as replichore ratios, GC skew, KOPS, and coding sequence distribution. Under standard culture conditions, the growth rates of these strains and the wild type were nearly comparable, and the ability for symbiotic nitrogen fixation was maintained. Spatiotemporal replicon organization and segregation were maintained in the triple replicon fusion strain. Deletion of the replication initiator-encoding genes, including the oriVs of pSymA and pSymB from this strain, resulted in a monopartite genome with oriC as the sole origin of replication, a strongly unbalanced replichore ratio, slow growth, aberrant cellular localization of oriC, and deficiency in symbiosis. Suppressor mutation R436H in the cell cycle histidine kinase CckA and a 3.2 Mbp inversion, both individually, largely restored growth, but only the genomic rearrangement recovered the symbiotic capacity. These strains will facilitate the integration of secondary replicons in S. meliloti and thus be useful for genome engineering applications, such as generating hybrid genomes.

Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection.

BACKGROUND: The government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space-time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022. METHODS: Spatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level. RESULTS: Malaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010-2011, and with P. vivax incidence in the north and central regions during 2012-2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south. CONCLUSIONS: Malaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area.

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-h short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Adaptive Gaussian Markov random fields for child mortality estimation.

The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks in mortality relative to their neighbors, which can lead to oversmoothing of U5MR estimates. In this paper, we develop a spatial and temporal smoothing approach based on Gaussian Markov random field models which incorporate knowledge of these expected shocks in mortality. We demonstrate the potential for these models to improve upon alternatives not incorporating knowledge of expected shocks in a simulation study. We apply these models to estimate U5MR in Rwanda at the national level from 1985 to 2019, a time period which includes the Rwandan civil war and genocide.

Nonparametric second-order estimation for spatiotemporal point patterns.

Many existing methodologies for analyzing spatiotemporal point patterns are developed based on the assumption of stationarity in both space and time for the second-order intensity or pair correlation. In practice, however, such an assumption often lacks validity or proves to be unrealistic. In this paper, we propose a novel and flexible nonparametric approach for estimating the second-order characteristics of spatiotemporal point processes, accommodating non-stationary temporal correlations. Our proposed method employs kernel smoothing and effectively accounts for spatial and temporal correlations differently. Under a spatially increasing-domain asymptotic framework, we establish consistency of the proposed estimators, which can be constructed using different first-order intensity estimators to enhance practicality. Simulation results reveal that our method, in comparison with existing approaches, significantly improves statistical efficiency. An application to a COVID-19 dataset further illustrates the flexibility and interpretability of our procedure.

Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development.

Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs' dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model's explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing.

Both nutrients and macrophytes regulate organic carbon burial: Insights from high-resolution spatiotemporal records of a large shallow lake (Baiyangdian) in eastern China.

Both ecological regime shifts and carbon cycling in lakes have been the subject of global debates in recent years. However, the direct linkage between them is poorly understood. Lake Baiyangdian, a representative large shallow lake with the coexistence of a macrophyte-dominated area (MDA) and an algae-dominated area (ADA) in eastern China, allowing better understanding of the relationship between regime shifts and organic carbon (OC) burial in lakes. On the basis of Bayesian isotopic mixing modelling of C/N ratios and δ13C values, the sediment OC is primarily of autochthonous origin. The mean OC burial rate (OCBR) was 39 g C m-2 yr-1 before eutrophication occurred in 1990 and increased approximately 2.7-fold to 106 g C m-2 yr-1 after eutrophication. Partial least squares path modelling revealed that this change can be largely attributed to enhanced primary productivity and rapid burial as a result of intensified human perturbation. In terms of spatial patterns, the OCBR was greater in the MDA than in the ADA, which may be related to the different burial and mineralization processes of debris from macrophytes and algae. It then deduced that a decrease in the OCBR and an increase in the mineralization rate might have occurred after a shift from a macrophyte-dominated state to an algae-dominated state. Our findings highlight that eutrophication generally increases OC burial by enhancing lake primary productivity. However, once nutrient levels reach a critical range, lake ecosystems may shift from a macrophyte-dominated state to an algae-dominated state, which can lead to a significant reduction in the carbon burial capacity of lakes. Therefore, more attention should be given to avoiding shifts in eutrophic lakes, as such shifts can alter carbon cycling.

Spatiotemporal and weather effects on the reproductive success of piping plovers on Prince Edward Island, Canada.

Piping plovers (Charadrius melodus sp.) rank among North America's most endangered shorebird species, facing compounding environmental challenges that reduce habitat availability and suppress recruitment and survival rates. Despite these challenges, research on the direct effects of climate variability and extremes on their breeding ecology remains limited. Here, we employ a spatiotemporal modelling approach to investigate how location, nest timing and weather conditions influence reproductive success rates in a small breeding population of C. m. melodus in Prince Edward Island (PEI), Canada from 2011 to 2023. Analysis of 40 years of monitoring records from a subset of nesting sites revealed that flooding and predation have been persistent sources of reproductive failures in this population, with unexplained losses increasing in recent years. Contrary to our hypotheses, our modelled results did not support a negative impact of extreme high temperatures and strong precipitation events on reproductive outcomes. Instead, we identified a positive effect of T MAX and no effect of strong precipitation, perhaps due to limited exposure to extreme high temperatures (>32°C) and context-specific risks associated with precipitation-induced flooding. However, trends in regional climate change are likely to increase exposure to-and the influence of-such factors in the near future. Our models also identified spatiotemporal variability in apparent hatch success over the study period, as well as worse hatch outcomes across popular beachgoing regions and for delayed nesting attempts. While our results offer preliminary insights into factors affecting breeding success in this population, further research will be imperative to enhance understanding of constraints on recruitment. To this end, we encourage the collection and analysis of additional time-series data of prey populations, human activities, fine-scale weather data and predator/flood risks associated with each nest on PEI.