Spatial Principal Component Analysis Research Articles

A new look at genetic variability in pomegranate: landscape genetics and structural equation modeling (SEM)

Pomegranate (Punica granatum L.) is an important horticultural plant in Iran and has been a food source since ancient times. Despite several genetic diversity studies conducted on these taxa, there are no reports on the landscape genetics of these important plants in Iran, nor is there any information on the effects of environmental, spatial, and climatic variables on the genetic structure of pomegranate. Therefore, this study used genetics, structural equation modeling (SEM), and species distribution modeling (SDM) approaches to explore the above-mentioned objectives. We used both cytogenetic and molecular analyses (SSR and SCoT markers) of several pomegranate samples along with environmental, spatial, and climatic variables. The redundancy analysis (RDA) analysis showed a significant positive association between terminal chiasmata with the longitudinal distribution of the pomegranate accession, while total chiasma number and interstitial chiasmata have an association with the altitude. The spatial PCA analysis indicated the role played by both global and local spatial variables in the cytogenetic variability like the univalent and terminal chiasmata formation. The Mantel test indicates that the cytogenetic difference in pomegranate plants is not related to their geographical distance, while the Moran I test result shows that geographical regions with similar spatial conditions affect the cytogenetic structuring of pomegranate plants in a similar way. SEM methods showed a strong negative association between bivalent formation and the environmental variables, while they showed both positive and negative associations between some of the environmental variables and different kinds of chiasmata. The sPCA analysis of molecular data showed a significant global, and local, spatial structuring of the molecular data and the genetic cline formation within the pomegranate accessions studied. Similarly, the spatial PCA analysis of climate variables, precipitation, and temperature revealed a significant structuring of genetic diversity and the formation of genetic clines by these factors. RDA analysis identified some of the loci that are significantly associated with temperature and precipitation. Therefore, both geographical/ or spatial variables and climate factors contribute to the genetic structure of the pomegranate accessions studied. SEM models based on genetic data revealed a positive association between environmental and climate variables with genetic diversity parameters and SSR-SCoT molecular loci studied. Climate change is unlikely to hurt pomegranate growing areas due to the high genetic diversity of pomegranate plants and the diverse genetic lineages present in the country.

Central Asia's desertification challenge: Recent trends and drives explored with google earth engine

Achieving land degradation neutrality (LDN) is considered critical to achieving sustainable development goals. However, further research and an improved large-scale desertification assessment framework are needed. We constructed a comprehensive land desertification status index (LDSI) to assess desertification and its driving mechanisms in Central Asia from 2001 to 2020. Our spatial distance models and principal component analysis were verified by combined field data. The results indicate that desertification levels in Central Asia fluctuated, decreasing from south to north and increasing from west to east. The extent of increased desertification (45.75%) and recovery (45.65%) were nearly equivalent. According to optimal parameters-based geographical detectors, climate factors are the main contributors to desertification, the most significant being reference evapotranspiration. We also found regional differences in the driving mechanisms of desertification. Based on Theil–Sen, Mann–Kendall, and Hurst tests, future desertification intensification of 9.69% is possible, with hotspots mainly in northwestern Central Asia, while 9.73% restoration may occur in other areas. The findings have relevance for achieving LDN. Policymakers should monitor the impact of global climate change and develop recovery measures based on comprehensive consideration of local climate, soil, and terrain conditions.

Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

Large-scale assessment of genetic structure to assess risk of populations of a large herbivore to disease.

Chronic wasting disease (CWD) can spread among cervids by direct and indirect transmission, the former being more likely in emerging areas. Identifying subpopulations allows the delineation of focal areas to target for intervention. We aimed to assess the population structure of white-tailed deer (Odocoileus virginianus) in the northeastern United States at a regional scale to inform managers regarding gene flow throughout the region. We genotyped 10 microsatellites in 5701 wild deer samples from Maryland, New York, Ohio, Pennsylvania, and Virginia. We evaluated the distribution of genetic variability through spatial principal component analysis and inferred genetic structure using non-spatial and spatial Bayesian clustering algorithms (BCAs). We simulated populations representing each inferred wild cluster, wild deer in each state and each physiographic province, total wild population, and a captive population. We conducted genetic assignment tests using these potential sources, calculating the probability of samples being correctly assigned to their origin. Non-spatial BCA identified two clusters across the region, while spatial BCA suggested a maximum of nine clusters. Assignment tests correctly placed deer into captive or wild origin in most cases (94%), as previously reported, but performance varied when assigning wild deer to more specific origins. Assignments to clusters inferred via non-spatial BCA performed well, but efficiency was greatly reduced when assigning samples to clusters inferred via spatial BCA. Differences between spatial BCA clusters are not strong enough to make assignment tests a reliable method for inferring the geographic origin of deer using 10 microsatellites. However, the genetic distinction between clusters may indicate natural and anthropogenic barriers of interest for management.

Are Regions Conducive to Photovoltaic Power Generation Demonstrating Significant Potential for Harnessing Solar Energy via Photovoltaic Systems?

To achieve the goals of carbon peak and carbon neutrality, Xinjiang, as an autonomous region in China with large energy reserves, should adjust its energy development and vigorously develop new energy sources, such as photovoltaic (PV) power. This study utilized data spatiotemporal variation in solar radiation from 1984 to 2016 to verify that Xinjiang is suitable for the development of PV power generation. Then, the averages of the solar radiation, sunshine duration, and other data in the period after 2000 were used to assess the suitability of Xinjiang, based on spatial principal component analysis (SPCA). Finally, the theoretical power generation potential, fossil fuel reduction, and CO2 emissions reduction were estimated. The results are as follows: (1) In terms of temporal variation, the solar radiation in Xinjiang decreased (1984–2002), increased (2002–2009), and decreased again (2009–2016), but the fluctuations were not statistically significant. In terms of spatial distribution, the Kunlun Mountains in southern Xinjiang had the highest solar radiation during the span of the study period. Hami and Turpan, in eastern Xinjiang, had sufficiently high and stable solar radiation. (2) The area in Xinjiang classed as highly suitable for solar PV power generation is about 87,837 km2, which is mainly concentrated in eastern Xinjiang. (3) In the situation where the construction of PV power plants in Xinjiang is fully developed, the theoretical potential of annual solar PV power generation in Xinjiang is approximately 8.57 × 106 GWh. This is equivalent to 2.59 × 109 tce of coal. Furthermore, 6.58 × 109 t of CO2 emissions can be reduced. PV power generation potential is approximately 27 times the energy consumption of Xinjiang in 2020. Through the suitability assessment and calculations, we found that Xinjiang has significant potential for PV systems.

Integrating ecosystem service supply–demand and ecological risk assessment for urban planning: A case study in Beijing, China

The balance between ecosystem service supply–demand and landscape ecological risk are two crucial aspects influencing the sustainable development of regional ecosystems. Current evaluations ignore these aspects and lead to partial landscape planning in urban areas. As a result, a framework was built for regional planning in urban areas based on multidimensional evaluations of ecosystems for high-efficiency land use planning. First, to determine the ecosystem service balance in regional scale, the supply and demand of five ecosystem services were quantified—biodiversity conservation, carbon sequestration, water conservation, food production, and landscape recreation. Second, landscape ecological risks were identified using the “natural–social–landscape” research framework combined with spatial principal component analysis. Third, spatial clustering analysis of ecosystem service supply–demand ratios and landscape ecological risks was then performed through spatial autocorrelation, which aimed to identify areas for protection and restoration priority. Finally, GeoDetector was used to determine the main factors influencing ecosystem service balance and landscape ecological risks and develop targeted planning measures. The results revealed an imbalance in total ecosystem service supply–demand in Beijing's plain area, coupled with a high level of landscape ecological risk. The combined ecosystem service supply–demand ratios exhibited a significant negative correlation and aggregation with landscape ecological risk, and the significant area accounted for approximately 31.9% of the total area. Priority areas for protection and restoration accounted for 10.39% and 19.94%, respectively, of the total regional area. Land use, distance to settlements, and vegetation cover were the primary environmental variables influencing the distributions of the combined ecosystem service supply–demand ratios and landscape ecological risk. So, this study emphasizes the need to strengthen intensive land use and prevent the uncontrolled expansion of urban land and settlements. It is crucial to enhance urban green infrastructure construction, promote the development of biodiversity-friendly agriculture, and implement measures such as afforestation restoration to increase regional vegetation cover. Overall, by integrating a framework of ecosystem services and landscape ecological risk, a comprehensive scientific reference can be provided for the efficient land use and sustainable development planning in urban areas.

Evaluation of ecological carrying capacity and construction of ecological security pattern in West Liaohe River Basin of China

The West Liaohe River Basin (WRLB) is a typical agro-pastoral interlaced zone in Northeast China where excessive economic development activities brought great pressure on the ecosystem and caused a series of ecological problems, having a negative effect on regional ecological carrying capacity (ECC). The rational construction of ecological security pattern (ESP) is an effective way to improve regional ECC and alleviate the contradiction between ecological protection and economic development. In this study, taking the WLRB as an example, spatiotemporal changes of ECC in the WLRB from 2000 to 2020 were explored by using spatial principal component analysis (SPCA). Furthermore, ecological sources were determined by coupling ECC evaluation results with important ecological patches such as natural protected areas, and the minimum cumulative resistance (MCR) model combined with the circuit theory method were employed to identify ecological security zoning, ecological corridors and key ecological nodes. Then a comprehensive ESP of the basin was constructed. The results show as follows: 1) The ECC of the overall WLRB was at medium carrying level, yet was spatially differentiated, which decreased from the periphery to the center of the basin in general. ECC showed a fluctuating upward trend from 2000 to 2020. 2) The ecological source area of the WLRB was 21926.91 km², accounting for 17.33% of the total study area. The ecological sources were seriously fragmentated and mainly distributed in the mountain area. The area of ecological conservation zone, optimized buffer zone, ecological transition zone, ecological prevention and control zone were 55672.69 km², 32662.24 km², 23862.84 km² and 12021.04 km², accounting for 44.82%, 26.29%, 19.21% and 9.68%, respectively. We also extracted 95 ecological corridors (with a total length of 3130.97 km), 49 ecological pinch points (with a total area of 200.33 km²) and 30 ecological barrier points (with a total area of 318.30 km²), constituting a “points-lines-planes” comprehensive ecological security pattern. This research can provide scientific reference for the protection and restoration of ecological environment and regional sustainable development.

Ecological Vulnerability Assessment and Spatiotemporal Characteristics Analysis of Urban Green-Space Systems in Beijing–Tianjin–Hebei Region

The evaluation and analysis of the ecological vulnerability of urban green-space systems are conducive to the sustainable development of urban green-space systems. Taking the urban green-space system in the Beijing–Tianjin–Hebei region in 2010, 2015, and 2020 as the research object, an ESSR model was first constructed, and a total of ten types of impact factors were integrated into the four dimensions of “Exposure, Sensitivity, State and Response”. The weight of the impact factors was objectively determined via spatial principal component analysis, and the ecological vulnerability of the urban green-space system was evaluated via superposition analysis; the evaluation’s results were graded. Moreover, the transfer matrix, center-of-gravity migration model, standard deviation ellipse, and spatial autocorrelation analysis were used to study the temporal and spatial variation characteristics of the evaluation results; then, the driving force of impact factors was analyzed based on a geographical detector. Finally, the rationality of the evaluation results was verified using the changing trend of the remote sensing ecological index (RSEI). The results show that the ecological vulnerability of the urban green-space system in the Beijing–Tianjin–Hebei region has decreased year by year for the past ten years. The distribution law of “Hebei surrounds Beijing and Tianjin” is presented in this space, and 2015 is the mutation node. In 2010, the moderately and severely vulnerable areas of Hebei surrounded the mildly vulnerability areas of Beijing and Tianjin. Moreover, in 2015 and 2020, the mildly vulnerable areas of Hebei surrounded the severely vulnerable areas of Beijing and Tianjin. Vulnerability expands slightly in the east–west direction and shrinks slightly in the north–south direction, and the center of gravity shifts towards Beijing year by year along the northeast direction. Moreover, the spatial distribution of vulnerability shows significant positive spatial autocorrelations and exhibits very obvious agglomeration. In addition, vulnerability is the result of the combined effect of various factors, and education degree, human disturbance index, and annual average precipitation are the dominant factors. The analysis results provide a reference for the effective application and sustainable development of urban green-space ecological functions.

Spatially Explicit climate change vulnerability assessment of ecological systems along altitudinal gradients in the Indian Himalayan region

Spatial ecological vulnerability assessments are important for devising management plans for ecological systems to maintain the flow of ecosystem services, especially in the mountains. Himalayan mountains, being a fragile ecosystem, have considerable ecological value, however, information on ecological vulnerability is lacking. The present evaluation attempts to prioritize ecosensitive zones by assessing the altitudinal ecological vulnerability of Garhwal Himalaya, India by devising a framework using a mix of bio-physical parameters. Eight bio-physical parameters, viz., Land Use and Land Cover (LULC), slope, aspect, landscape fragmentation, temperature, precipitation, soil moisture, and Normalized Difference Vegetation Index (NDVI)) were identified and considered to assess vulnerability. Spatial Principal Component Analysis (SPCA) results in five Principal Components (PCs), which were integrated and weighted for the calculation of the Ecological Vulnerability Index (EVI). The analysis results differential vulnerability across the altitudes with above 2000m altitude showed severe vulnerability and classified as potential vulnerability (36.25 %), slight vulnerability (35.25 %), medium vulnerability (20.25 %), high vulnerability (8.25 %) and severe vulnerability (0.25 %). The vulnerable regions were prioritized based on their ecological vulnerability as per the classes viz., region of strict protection (24.70 %), region of focal protection (60.41%) and region of composite development (14.89 %). The study suggetsed priority conservation and management actions for addressing the vulnerable areas for sustainable development of the region. The study provides valuable insights of vulnerability and associated protection measures leading to devising the most suitable management strategies for the sustainability of the ecological systems.

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

AbstractThe innovative combination of additive manufacturing (AM) and continuous fiber‐reinforced polymer composites (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties, but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced‐order models and machine learning (ML) to extract the structure–property (SP) linkages between microstructures and macroscopic elastic properties of AM‐CFRPCs. The initial step involves generating microstructural 2D cross‐sections and representative volume elements (RVEs) with random fiber and pore distributions based on the minimum potential method. Then, finite element (FE) calculations are performed on RVEs to obtain nine macroscopic elastic properties. Following that, the quantification and dimensionality reduction of the 2D cross‐sectional dataset are conducted separately using two‐point spatial correlations and principal component analysis (PCA). Finally, a Bayesian optimized composite kernel support vector regression (CK‐SVR) algorithm is used to effectively establish complex mapping relationships between the reduced‐order representations of the microstructures and the mechanical properties. Despite the reduced‐order dataset containing only 3–6 variables, the framework generates an interpretable model exhibiting excellent accuracy with all predicted R2 values surpassing 0.91. Therefore, this framework presents a prospective solution for expediting the design and optimization of AM‐CFRPCs.Highlights A materials informatics scheme is proposed to predict the 9 elastic properties of AM‐CFRPCs. Microstructures are quantified and dimensionally reduced by two‐point statistics and PCA. SP linkages are established between 2D cross‐sections and 3D macromechanical properties. Modified CK‐SVR exhibits higher prediction accuracy compared to conventional models.

Identifying Ecological Security Patterns Considering the Stability of Ecological Sources in Ecologically Fragile Areas

Ecological security patterns (ESPs) provide an effective spatial approach for identifying critical conservation areas and ensuring regional ecological security. However, prior research has not paid much attention to the importance of the stability of ecological sources in time-series changes, which is especially critical for maintaining ecological functions in ecologically fragile areas. Focusing on the Ningxia Hui Autonomous Region (NHAR) of China, this study evaluated the spatiotemporal change patterns in ecosystem services importance (ESI) from 2000 to 2020, integrating the spatial principal component analysis (SPCA) and circuit theory to propose a novel ESP construction framework that aims to address the issue of insufficient consideration of source stability. A total of 93 stable ecological sources were identified, with the capacity to ensure the continuous provision of high-level ecosystem services and resistance to external disturbances. The extraction of 234 ecological corridors and 430 ecological nodes effectively enhanced the stable flow of ecological processes and connectivity. The stable ESP, constituted by the above ecological elements, can serve as core ecological space and basic skeleton to maintain the regional sustainable landscape. This study provides scientific references for identifying key priority conservation areas and formulating targeted ecological conservation and restoration strategies in ecologically fragile areas.

Land Use and Cover Changes in Brazilian Biomes between 1985 and 2018

In the last decades, there have been changes in land use and cover in the Brazilian territory, many resulting from anthropic activities. However, these changes impact the environmental system, compromising biodiversity and ecological services. In this context, integrative geographic information about environmental changes in the Biomes allows the identification of the main Spatio-temporal changes in land use and cover and can contribute to efficient environment planning. Thus, this paper proposes to describe and synthesize the Spatio-temporal changes in Brazilian Biomes between 1985-2018 using spatial statistics techniques and Principal Component Analysis with data from the MapBiomas project. It has employed the Google Earth Engine and QGIS for the analysis, with emphasis on spatial statistics and Principal Components Analysis which enabled the identification of the main changes in land use and cover. From the Principal Components Analysis, it was possible to reduce the data set with 34 images to two Principal Components that represent significant percentages of the variance of the original database. The results showed that the Brazilian Biomes had undergone significant changes over 34 years. The main vectors that drove these transformations were anthropic activities, such as urban growth, the advancement of agriculture, and cattle ranching in the countryside. Amazon and Cerrado present the largest area altered in square kilometers. And the fastest changes occurred on Amazon, Cerrado, and Pantanal. This work presents a synthesis of the main patterns and spatial-temporal changes in the use and cover of Brazilian Biomes, which can assist in the national environmental planning.

A comprehensive drought index based on spatial principal component analysis and its application in northern China.

In the background of the greenhouse effect, drought events occurred more frequently. How to monitor drought events scientifically and efficiently is very urgent at present. In this study, we employed the Vegetation Water Supply Index (VSWI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) as individual variables to construct a composite drought index (CDI) using spatial principal component analysis (SPCA). The validity of CDI was assessed using gross primary productivity (GPP), soil moisture (SM), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI). CDI was subsequently used for drought monitoring in northern China from 2011 to 2020. The results showed that (1) at a 99% confidence level, the Pearson correlation coefficients between CDI and GPP was 0.72, while the value between CDI and SM was 0.69, which indicated the relationship between SM, GPP, and CDI was significant. (2) We compared CDI with other variables such as Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Drought Index (CDI) and found that the monitoring result of CDI was more sensitive, which indicated that the proposed CDI had a better effect in local drought monitoring. (3) The results of CDI showed that the drought status in the northern region during 2011-2020 lasted from March to October, and the high severe drought period generally occurs in March-May and September-October, with low severe drought in June-August.

Abstract C107: Use of spatial transcriptomics to identify molecular features associated with African American heritage in pancreatic cancer

Abstract Pancreatic cancer is the third leading cause of cancer-related deaths in the US, with a 5-year survival rate of 12%. Black or African American patients (BAA) have a 20% higher incidence of pancreatic cancer, and recent studies suggest that there are biological factors that may influence this disparity, in addition to complex socioeconomic factors. Pancreatic tumors are highly enriched with stroma and exhibit significant inter- and intra-patient morphological and molecular heterogeneity. To improve the care for all patients, we need to better understand the disease mechanisms by comprehensively mapping different spatial locations within individual patients’ tumors and determining whether there are common patterns of pathogenesis across diverse patient populations. In this study, we utilized the 10X Visium spatial transcriptomics platform on eight treatment-naïve primary pancreatic tumors. We tested multiple published bioinformatic packages, including SpatialDE, SPARK-X, Spatial PCA, and Cottrazm, and optimized a multi-sample integrated analysis pipeline that allows us to automatically segment tumor lesions, infer major cell types in regions of interest on tissues, detect spatially variant genes, and assess associations between tissue morphological phenotypes and transcriptomic features. Importantly, our study cohort includes four White patients and four BAA patients. Through the comparison of spatial transcriptomes between White and BAA patients, we identified several genes displaying differential expression in tumor lesions of distinct racial groups, including Trefoil Factor 1 (TFF1) and CYP3A5. These genes present potential candidates for further investigation to elucidate the biological mechanisms underlying racial disparities in pancreatic cancer. Citation Format: Yuesong Wu, Aoqi Xie, Ian Loveless, Madison George, Kendyll Gartrelle, Julie Clark, Daniel Salas-Escabillas, Rupen Shah, David Kwon, Ralph Francescone, Débora B. Vendramini Costa, Howard Crawford, Brian Theisen, Yuehua Cui, Ling Huang, Nina G. Steele. Use of spatial transcriptomics to identify molecular features associated with African American heritage in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C107.

Construction of a cold island network for the urban heat island effect mitigation

The urban heat island (UHI) effect seriously challenges sustainable urban development strategies and livability. Numerous studies have explored the UHI problem from the perspective of isolated blue and green patches, ignoring the overall function of cold island networks. This study aims to explore the construction method of cold island network by integrating scattered cold island resources, rationally guiding urban planning and construction, and providing effective ideas and methods for improving the urban thermal environment. Taking the central city of Fuzhou as an example, the identification of the cold island core source (CICS) was optimized by applying relative land surface temperature (LST), morphological spatial pattern analysis, and landscape connectivity analysis. The combined resistance surface was constructed based on a spatial principal component analysis. Subsequently, the cold island network was constructed by applying circuit theory and identifying the key nodes. The results showed that the central and eastern parts of the study area experienced the most significant UHI effects and there was a tendency for them to cluster. Overall, 48 core sources, 104 corridors, 89 cooling nodes, and 34 heating nodes were identified. The average LST of the CICSs was 28.43 °C, significantly lower than the average LST of the entire study area (31.50 °C), and the 104 cold corridors were classified into three categories according to their importance. Different targeting measures should be adopted for the cooling and heating nodes to maintain the stability of the cold island network and prevent the formation of a heat network. Finally, we suggest a model for urban cold island network construction and explore methods for mitigating issues with UHI to achieve proactive and organized adaptation and mitigation of thermal environmental risks in urban areas, as well as to encourage sustainable urban development.

Decomposing the late positive potential to cannabis cues in regular cannabis users: A temporal-spatial principal component analysis.

Cannabis use disorder (CUD) is increasing in the United States, yet, specific neural mechanisms of CUD are not well understood. Disordered substance use is characterized by heightened drug cue incentive salience, which can be measured using the late positive potential (LPP), an event-related potential (ERP) evoked by motivationally significant stimuli. The drug cue LPP is typically quantified by averaging the slow wave's scalp-recorded amplitude across its entire time course, which may obscure distinct underlying factors with differential predictive validity; however, no study to date has examined this possibility. In a sample of 105 cannabis users, temporo-spatial Principal Component Analysis was used to decompose cannabis cue modulation of the LPP into its underlying factors. Acute stress was also inducted to allow for identification of specific cannabis LPP factors sensitive to stress. Factor associations with CUD severity were also explored. Eight factors showed significantly increased amplitudes to cannabis images relative to neutral images. These factors spanned early (~372 ms), middle (~824 ms), and late (>1000 ms) windows across frontal, central, and parietal-occipital sites. CUD phenotype individual differences were primarily associated with frontal, middle/late latency factor amplitudes. Acute stress effects were limited to one early central and one late frontal factor. Taken together, results suggest that the cannabis LPP can be decomposed into distinct, temporal-spatial factors with differential responsivity to acute stress and CUD phenotype variability. Future individual difference studies examining drug cue modulation of the LPP should consider (1) frontalcentral poolings in addition to conventional central-parietal sites, and (2) later LPP time windows.

Multi-dimensional landscape ecological risk assessment and its drivers in coastal areas

The eastern coastal areas of Japan are threatened by multiple ecological risks due to frequent natural disasters, climate changes, human activities, etc. Identification spatio-temporal variations and driving mechanisms of landscape ecological risk could be used as significant basis for policymakers. In this study, taking the eastern coastal areas of Japan affected by the 2011 Great East Japan Earthquake and Tsunami Disaster as the study area, the “Nature-Landscape Pattern-Human Society” (NA-LP-HS) multi-dimensional ecological risk assessment framework was established to analyze the spatio-temporal patterns, and identity driving factors using spatial cluster analysis and spatial principal component analysis (SPCA) based on ArcGIS from 2009 to 2021. The findings revealed the distinct geographic patterns in landscape ecological risk, with a noticeable decline from the southwest to the northeast. During the period from 2009 to 2015, the driving factors leading to a sharp risk increase were natural disasters and vegetation coverage. These high-risk areas were concentrated in Sendai Bay and its surroundings. From 2015 to 2021, ecological instability was primarily attributed to a reduction in vegetation coverage, the occurrence of natural disasters, and heightened rainfall erosion. These high-risk areas were mainly clustered within the Tokyo-centered urban agglomeration. Spatial clustering of ecological risks was obvious across all time periods. The key factors contributing to the clustering of high ecological landscape risks focused on the “landscape pattern” criterion, specifically including vegetation coverage, land use land cover. This study demonstrated the ability of multi-dimensional ecological risk assessment to identify high-risk areas and driving factors, and these results could provide a visual analysis and decision-making basis for sustainable development of coastal areas.

Impact of grassland storage balance management policies on ecological vulnerability: Evidence from ecological vulnerability assessments in the Selinco region of China

The Selinco region is a highland grassland ecosystem with a unique lake system and diverse flora and fauna. However, it is highly vulnerable to degradation due to its high altitude and harsh climate. In recent years, the region has been subjected to increasing external pressures, including climate change, human activities, and natural disasters. Therefore, conducting an ecological vulnerability assessment is essential to ensure the sustainable development of the region. To achieve this, we performed a comprehensive integrated spatial principal component analysis (SPCA) of 12 representative ecological vulnerability indicators to assess the ecosystem of the region. This approach facilitated a comprehensive assessment of the ecological status of the region. Our study indicated that population and livestock density are the most important factors influencing environmental vulnerability. While the region has shown a trend of ecological recovery and stability over the past 20 years, some areas still experience ecological deterioration, emphasizing the importance of increasing efforts for to ecological conservation and restoration. Furthermore, we observed that different ecologically fragile areas have experienced varying degrees of transformation, underscoring the importance of more precise ecological conservation policies. At the county level, Nima and Shuanghu counties require focused attention and remedial measures to address severe ecological degradation, while other counties have demonstrated a trend towards ecological restoration, which could be attributed to the implementation of a balanced grass storage management policy that mitigates the impact of human activities. Therefore, further research is essential to determine the persistence of ecological trends and to identify long-term conservation and sustainable use solutions.

Differential responses of crop yields to multi-timescale drought in mainland China: Spatiotemporal patterns and climate drivers

Increasingly frequent and severe droughts pose a growing threat to food security in China. However, our understanding of how different crops respond to multi-timescale drought under varying climatic conditions remains limited, hindering effective drought risk management. To address this knowledge gap, we applied spatial principal component analysis (SPCA) to unveil spatiotemporal patterns in annual yields of major grain crops (rice, wheat, maize) and cotton in response to multi-timescale drought, as indicated by the standardized precipitation evapotranspiration index (SPEI) across China. Subsequently, predictive discriminant analysis (PDA) was employed to identify the primary climatic factors driving these response patterns. The findings indicated that drought-induced interannual variability of crop yields were spatially and temporally heterogeneous, closely tied to the timescale used for drought assessment. Crop types displayed distinct responses to drought, evident in the variations of months and corresponding timescales for their strongest reactions. The initial three principal components, capturing over 65 % of drought-related yield variance, unveiled short- to medium-term patterns for rice, maize, and cotton, and long-term patterns for wheat. Specifically, rice was highly susceptible to drought on a 4-month timescale in September, wheat on a 6-month timescale in May, maize on a 3-month timescale in August, and cotton on a 3-month timescale in September. Moreover, the first three discriminant functions explaining over 90 % of the total variance, effectively distinguish spatiotemporal crop yield response patterns to drought. These patterns primarily stem from seasonal climatic averages, with water balance (precipitation minus potential evapotranspiration) and temperature being the most influential variables (p < 0.05). Interestingly, we observed a weak correlation between drought severity and crop yield in humid conditions, with responses tending to manifest over longer timescales. These findings enhance our comprehension of how drought timescales impact crop yields in China, providing valuable insights for the implementation of rational irrigation management strategies.

Studies on genetic diversity, gene flow and landscape genetic in Avicennia marina: Spatial PCA, Random Forest, and phylogeography approaches

Mangrove forests grow in coastal areas, lagoons, estuaries, and deltas and form the main vegetation in tidal and saline wetlands. Due to the mankind activities and also changes in climate, these forests face degradations and probably extinction in some areas. Avicennia marina is one of the most distributed mangrove species throughout the world. The populations of A. marina occur in a limited region in southern parts of Iran. Very few genetic and spatial analyses are available on these plants from our country. Therefore, the present study was planned to provide detailed information on Avicennia marina populations with regard to genetic diversity, gene flow versus genetic isolation, effects of spatial variables on connectivity and structuring the genetic content of trees populations and also identifying adaptive genetic regions in respond too spatial variables. We used SCoT molecular markers for genetic analyses and utilized different computational approaches for population genetics and landscapes analyses. The results of present study showed a low to moderate genetic diversity in the studied populations and presence of significant Fst values among them. Genetic fragmentation was also observed within each province studied. A limited gene flow was noticed among neighboring populations within a particular province. One population was almost completely isolated from the gene flow with other populations and had peculiar genetic content.Spatial PCA analysis revealed both significant global and local genetic structuring in the studied populations. Spatial variables like humidity, longitude and altitude were the most important spatial features affecting genetic structure in these populations.