Patterns Of Spatial Variation Research Articles

Spatial variability and changing pattern of wellbeing of women in West Bengal, India: a sociodemographic perspective

Spatial variability and changing pattern of wellbeing of women in West Bengal, India: a sociodemographic perspective

Individual Variability in the Structural Connectivity Architecture of the Human Brain.

The human brain exhibits high degree of individual variability in both its structure and function, which underlies inter-subject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the hetero-modal association cortex but less variable in the unimodal cortices. Structural connectivity is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in structural connectivity (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the structural connectivity exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain structural connectivity and link to the prediction of individual cognitive function in adult subjects.Significance Statement White matter connectivity between grey matter regions plays an important role in integrating information from distributed regions when individuals performing complex cognitive functions. Unique white matter connectivity is the neuroanatomical identity of each individual. The authors systematically explored the spatio-temporal pattern of individual variability in structural connectivity, and the results show higher variability in structural connectivity relates to higher neuroplasticity. In addition, this study reveals that structural connectivity involved in executive function and attention task is different among individuals and highlights the importance of individualized statistical methods for mapping neural pathway of complex cognition.

State-and-Evolution Detection Model for Characterizing Farmland Spatial Pattern Variation in Hengyang Using Long Time Series Remote Sensing Product

Analyzing farmland landscape pattern variations induced by human activities can support effective decision making by governments to improve land use efficiency. However, research on long-term and continuous spatial process changes in farmland is scarce, and spatial pattern changes in farmlands remain insufficiently understood. Moreover, studies in which researchers have utilized dynamic process analysis to describe farmlands are relatively limited. This study aimed to apply the state-and-evolution detection model (SEDM), generated from long-term remote sensing data, to characterize farmland spatial pattern variations in Hengyang City, Hunan Province. Annual farmland data from 1990 to 2022, change type samples, and auxiliary data were collected, and six types of spatial pattern variations (perforation, dissection, shrinkage, creation, enlargement, and aggregation) were defined for the study area. Subsequently, the SEDM was applied based on four landscape indices. Finally, spatiotemporal evolution features, namely evolution times, evolution duration, and dominant evolution pattern, were quantified. The farmland in the study area exhibited a generally upward trend with fluctuations. The maximum area was followed by shrinkage (S), perforation (P), and enlargement (E). In over 70% of the study area, fewer than three evolution times occurred over three decades. The dominant evolution patterns were P–S, S–P, and E–P for single evolution events, and P–S–P, S–P–S, and P–S–S for double events. The model achieved an overall accuracy of 85%, thus demonstrating its effectiveness in characterizing landscape pattern variations and providing valuable insights for researchers and policy makers to develop strategies for farmland protection.

Macro‐evolutionary dynamics dominated by dispersal promote the formation of regional biodiversity hotspot‐insights from hawkmoths (Lepidoptera: Sphingidae) in South China

AbstractAimRapid loss in global insect diversity has generated substantial public worry due to their critical ecological roles. However, there is controversy about the effectiveness of the global‐scale hotspots in guiding the conservation of diversity at the regional scale. Even worse, little is known about the knowledge of insect distributional dynamics in many understudied regions, such as East and Southeast Asia. Here, to guide for setting regional‐scale conservation priorities for insect diversity, we explore hawkmoths (Lepidoptera: Sphingidae) for their distributional dynamics and identify regional hotspots requiring protection.LocationSouth China (including Guangdong, Guangxi, Hainan, Hong Kong and Macau) and northern Vietnam (17°~26.5° N, 102°~117.5° E).MethodsSpecies distribution models were generated for 194 hawkmoth species based on 3597 occurrence records to predict their distributions. We calculated the spatial patterns of taxonomic and phylogenetic diversity and identified regional hotspots. Furthermore, the potential assembly mechanisms underlying insect diversity were explored by analysing the rates of speciation, extinction and dispersal between phyloregions.Results(a) The coastal regions of South China and northern Vietnam represent a regional hotspot of hawkmoths in East and Southeast Asia, with significantly higher α‐diversity than that in inland regions. (b) Dispersal played a more important role than local speciation and extinction in the formation of regional hawkmoth hotspots.Main ConclusionsIn this study, the ‘Out‐of‐the‐tropics model’ can explain the formation of the hawkmoth regional hotspots and the enhanced version of the ‘Pure dispersal model’ can explain the formation of the hotspots in Hainan Island. Compared with the local speciation and extinction, dispersal is the main driving factor that promoted the formation of the regional biodiversity hotspot of hawkmoths in South China. The case of Hainan Island suggests that protection within hotspots needs to account for specific regional macro‐evolutionary dynamics rather than indiscriminate coverage of identified hotspots.

Spatial patterns of macrozoobenthos diversity on sewage-impacted intertidal rocky shores in Central Patagonia, Argentina

The effects of untreated wastewater and shore levels on zoobenthic diversity, species accumulation, and abundance-biomass relationships were studied on intertidal rocky shores of central Patagonia, Argentina. Winter samples (2015) from two sewage-impacted and two reference sites showed that pollution disrupted macrozoobenthic diversity and zonation. A total of 83 taxa were identified, with polychaetes (31 species), mollusks (22), and crustaceans (21) being the most diverse. Impacted sites had greater intra-environmental diversity heterogeneity and higher abundance at the lower midlittoral level, indicating a shift towards lower intertidal levels. Species richness was lower and evenness was higher in the polluted mid-littoral level. ABC curves in the stressed areas showed moderate disturbances with variability across shore zones, while non-polluted rocky shores typically exhibit a higher cumulative faunal biomass than abundance. In impacted areas, chronic habitat changes and disturbances to the mussel matrix by Perumytilus purpuratus are likely responsible for differences in diversity and abundance-biomass relationships.

Phylogeography of Cold Water Soft Coral Alcyonium spp. (Anthozoa, Octocorallia: Alcyonacea) Between South America and the West Antarctic Peninsula.

The Antarctic marine environment has a unique geologic and climatic history that has contributed to the evolution of high species diversity. Given the current trend of environmental warming, understanding the history of Antarctic species is crucial for predicting the impact of climate change on ecosystem function. Soft corals are a group of striking presence in the benthic marine assemblages in the Southern Ocean, which is recognized as a biodiversity hotspot. DNA sequences (Cox1, mtMutS, and 28S rDNA) were utilized for molecular phylogenetic reconstructions, species delimitations, and divergence estimations to investigate the spatial patterns of genetic diversity in Alcyonium species in the southern South American-Antarctic region. Significant genetic divergence was observed between regions, with a clear genetic break between South America and the West Antarctic Peninsula and the identification of four putative species. Divergence time estimates indicated that Alcyonium's diversification began about 41.1 million years ago (Ma), coinciding with the opening of the Drake Passage and the formation of the Antarctic Circumpolar Current (ACC, ~42 Ma). This indicates that Alcyonium has persisted insitu for an extensive period, enduring a wide range of environmental conditions.

The effect of stress history on the critical shear stress of bedload transport in gravel-bed streams

Critical shear stress is a pivotal parameter that describes the bedload transport and riverbed stability. Recent studies indicate that different interevent periods in gravel-bed streams can lead to a realignment of riverbed structures, thereby disrupting the bedload transport. In this research, a series of flume experiments were conducted to study the effect of stress history on the critical shear stress of bedload transport in a gravel-bed channel. The results reveal the temporal-spatial variation patterns of critical shear stress. The critical shear stress has an increased trend after interevent with low flow and long duration. Conversely, a decreasing trend in critical shear stress observed after low flow and short duration conditions. After interevent durations with medium to high flow condition, no matter the duration, critical shear stress shows a decreasing trend. A significant positive correlation between the flow magnitude of the interevent period and the critical shear stress of the subsequent flood period is shown. Besides, critical shear stress has a significant spatial variation pattern on the gravel bed due to the presence of microtopography as revealed by the correlation analysis results among the spatial density of microtopography with different protrusion heights, riverbed statistical parameters, and critical shear stress. Moreover, the stress history, by adjusting the position of fine particles on the riverbed surface through different magnitudes and durations of the interevent period, affects the spatial density of the microtopography, thereby influencing the critical shear stress. The conventional bedload transport equations are deficit by only considering the riverbed slope or standard deviation of bed surface elevation. An optimization of the critical shear stress calculation method, based on parameter correlation analysis, is proposed, potentially enhancing the calculation accuracy of bedload transport in mountainous rivers.

Yield prediction through UAV-based multispectral imaging and deep learning in rice breeding trials

ContextPredicting crop yields with high precision and timeliness is essential for crop breeding, enabling the optimization of planting strategies and efficients resource allocation while ensuring food security. Current research in this field typically does not address the problem of yield prediction in the diverse context of breeding experiments involving numerous varieties. However, evaluating the performance of prediction models across multiple varieties is vital for further model refining and enhancing model robustness and adaptability. ObjectiveThis study aims to evaluate the performance of feature- and image-based yield prediction models for yields with multiple varieties to compare their capabilities and determine an appropriate timing for early yield prediction. MethodsThis study combines unmanned aerial vehicle (UAV)-based multispectral remote sensing imagery with machine learning and deep learning-based algorithms to develop rice yield prediction models across multiple varieties. The performances of both feature- and image-based models are evaluated. The feature-based models considered in this study include random forest (RF), deep neural network (DNN), and long short-term memory (LSTM) algorithms, and the image-based models are convolutional neural network (CNN) architectures, including both two-dimensional (2D) and three-dimensional (3D) CNN models. To assess the performance of the multi-variety crop yield prediction models thoroughly, this study considers two sampling scenarios: stratified sampling and group sampling. Results and conclusionsThe results show that the image-based deep learning models outperform the feature-based machine learning models, which indicates their superior robustness in multi-variety scenarios and highlights their significant potential of directly extracting spatiotemporal features from images for yield prediction. The results indicate that the multi-temporal 2D CNN model (i.e., the CNN-M2D model) can achieve the best yield prediction performance among all models, achieving RRMSE = 8.13 % and R2 = 0.73. The prediction results also demonstrate good consistency with the observed data, indicating an efficient capturing of spatial pattern variations in yield across different varieties. Based on the results, with the crops progressing along the growth stages, the accuracy of the yield prediction models improves gradually, achieving the best prediction performance during the flowering to grain-filling stage. Finally, according to the results, the optimal lead time for predicting rice yield is approximately one month before harvest. SignificanceOur study can provide a reference for the research community in yield prediction and high-yield variety selection in breeding trials.

Estimating dispersal rates and locating genetic ancestors with genome-wide genealogies.

Spatial patterns in genetic diversity are shaped by individuals dispersing from their parents and larger-scale population movements. It has long been appreciated that these patterns of movement shape the underlying genealogies along the genome leading to geographic patterns of isolation by distance in contemporary population genetic data. However, extracting the enormous amount of information contained in genealogies along recombining sequences has, until recently, not been computationally feasible. Here we capitalize on important recent advances in genome-wide gene-genealogy reconstruction and develop methods to use thousands of trees to estimate per-generation dispersal rates and to locate the genetic ancestors of a sample back through time. We take a likelihood approach in continuous space using a simple approximate model (branching Brownian motion) as our prior distribution of spatial genealogies. After testing our method with simulations we apply it to Arabidopsis thaliana. We estimate a dispersal rate of roughly 60km2 per generation, slightly higher across latitude than across longitude, potentially reflecting a northward post-glacial expansion. Locating ancestors allows us to visualize major geographic movements, alternative geographic histories, and admixture. Our method highlights the huge amount of information about past dispersal events and population movements contained in genome-wide genealogies.

An Investigation of the Spatial Arrangement of Mycotoxin Build-Up in Corn Stored Under Different Environmental Conditions

Mycotoxins are toxins produced by fungi that contaminate many key food crops as they grow in the field and during storage. Specific mycotoxins are produced by different fungi. Each type of fungus and mycotoxin have their own optimal temperatures and water activities for growth and production. The legislative limits for various mycotoxins in foodstuffs to protect human health vary between countries but all commodities have their levels evaluated based on the concentrations from one aggregated grain sample. This approach assumes that the variation in toxin levels is uniform and random without spatial trends. This study investigates the spatial distribution of four mycotoxins (aflatoxin, deoxynivalenol, fumonisin and zearalenone) in bins of clean and dirty corn when stored in an environmental cabinet for two months under different temperature and humidity conditions. The bins of clean and dirty corn each had 12 CO2/humidity/temperature sensors installed in three layers, and samples were extracted for mycotoxin analysis from locations close to each sensor following storage. Using Mann–Whitney U and Kruskal–Wallis H statistical tests, significant differences were found between mycotoxin levels attributable to the different environmental conditions and spatial locations of samples. Variations in aflatoxin and zearalenone concentrations were most pronounced for the range of temperature and humidity conditions chosen. By understanding the patterns of spatial variability in mycotoxin concentrations and identifying zones at high risk of contamination, as well as what conditions are favorable, targeted interventions could be implemented to reduce food waste. This work also has implications for how levels of mycotoxins in foodstuffs are sampled and measured.

Assessment of multidimensional diversity and conservation of threatened timber trees in China under climate change

Understanding spatial patterns of multidimensional diversity is crucial for effective conservation planning, especially in mountainous regions that are often biodiversity hotspots. This study focused on assessing the impact of climate change on the distribution of 110 threatened timber tree species in China, combining species distribution models and phylogenetic frameworks to quantify multidimensional diversity indices. The research aimed to evaluate the effectiveness of these indices in guiding the prioritization of protected areas and conservation strategies in the face of climate change. The findings revealed that Chinese protected areas are projected to experience a significant decline in their ability to protect the top 15 % hotspots for evolutionarily distinct and globally endangered (EDGE) species under future climate scenarios. This decline ranges from 17.11 % to 29.93 %, highlighting the urgent need for adaptive conservation strategies. Energy-related factors, particularly annual mean temperature and temperature annual range, were identified as key drivers of timber tree distribution patterns. Based on these results, the study advocate for the integration of multidimensional diversity indices into conservation priority evaluation frameworks and considering species' evolutionary history and vulnerability when determining regional biodiversity targets. This approach can help improve the effectiveness of conservation efforts in the face of ongoing climate change.

Urbanization and plant diversity in urban fringes: Differential responses across life forms

Urban fringe areas, as critical transition zones between urban expansion and natural environments, provide essential ecosystem services that enhance the overall health and sustainability of urban environments. However, previous studies have focused on urban built–up areas while neglecting the unique conditions of urban fringe zones. This study investigated the spatial distribution patterns of plant diversity across different life forms in Jiangdong New District and their responses to urbanization levels. The results indicate that hot and cold spots of diversity for trees and shrubs show similarity, while diversity in herbaceous plants differs significantly. Nighttime light intensity, POI (Point–of–Interest) density, and road density are primary influencing factors, with nighttime light intensity and POI density having the greatest combined impact, and herbs showing the highest sensitivity to these factors. Further analysis reveals that in the northwestern forest, grassland, and farmland mosaic areas, urbanization levels are positively correlated with tree and shrub diversity but negatively correlated with herb diversity. In the southeastern nursery planting area, urbanization levels consistently relate to the diversity of these three plant types. These findings underscore the necessity of adopting diverse conservation and management strategies in urban fringe areas to address the ecological needs and functional contributions of different plant types. This provides new perspectives on urban planning and ecological conservation that can help improve the overall health and sustainability of urban ecosystems.

Phylogeography of Microphis retzii (Bleeker, 1856) and Microphis brachyurus (Bleeker, 1854) in the Pacific.

Syngnathidae are a charismatic family of teleost fishes, represented by seahorses, seadragons, and pipefishes. Syngnathidae are mainly composed of marine species, but about 30 species of pipefishes inhabit freshwater insular environments of the Indo-Pacific realm. Recent research has shown that some freshwater pipefish species are amphidromous and exhibit high intraspecific divergences across their distribution range, like Microphis brachyurus (Bleeker, 1854) distributed from Sri Lanka to French Polynesia and Microphis retzii (Bleeker, 1856) distributed from Taiwan to Indonesia. In this study, we used the mitochondrial cytochrome oxidase I (COI) partial gene of 91 specimens of M. brachyurus and 30 specimens of M. retzii from localities representative of their respective distribution area to improve knowledge on the population structure of these two widespread species. Genetic species delimitation and phylogeographic analyses were combined to explore spatial patterns of genetic diversity across the distribution ranges of the two species. We have highlighted deep genetic structuring within the two species and relate these results to various biotic and abiotic factors. For M. brachyurus, the population in Polynesia is distinct from those in the West Pacific, suggesting its distinctiveness and recognition as an evolutionary significant unit (ESU). For M. retzii, three lineages are delimited in its range distribution, suggesting the existence of two distinct species in Southeast Asia (Bali/Java/Lombok and China/Taiwan). Pipefish species are particularly vulnerable to anthropogenic pressures (inherent to Syngnathidae and insular environments). The present results, revising species delimitation and geographic distribution, will help implement effective conservation and management measures.

Spatial pattern of woody plant species richness and composition in primary warm temperate evergreen forest in Kasugayama Hill, Japan

Plant species richness and composition are influenced by complex interactions between biotic and abiotic factors that operate on different spatial scales. Since spatial scales vary continuously in nature, it is expected that multiple factors simultaneously affect species richness and composition at an intermediate spatial scale (i.e., the mesoscale landscape level). Previous studies have shown that local topography and elevation are important factors for shaping intermediate spatial scale plant species richness; however, the relative importance of these factors has rarely been examined. Here, we used spatially explicit woody plant data to examine the factors that characterize the spatial pattern of primary evergreen forest biodiversity at the intermediate spatial scale. We found that the spatial pattern of species diversity in a predominantly warm temperate evergreen forest at the landscape level is mainly characterized by shifts in species composition along the elevation gradient. Our study also found that compositional shift along the elevational gradient was mainly caused by habitat specialization among congeneric species, suggesting that niche partitioning among closely-related species is a fundamentally important feature of the intermediate spatial scale species richness pattern. Furthermore, we found that specialization in a habitat of closely-related species can be established even within a limited environmental gradient. This suggests that biotic interactions among closely-related species may be an important factor driving habitat specialization, and biotic interactions may play an important role in shaping landscape-scale biodiversity patterns.

Remotely Sensed Variables Predict Grassland Diversity Better at Scales Below 1,000 km as Opposed to Abiotic Variables That Predict It Better at Larger Scales

AbstractGlobal spatial patterns of vascular plant diversity have been mapped at coarse grain based on climate‐dominated environment–diversity relationships and, where possible, at finer grain using remote sensing. However, for grasslands with their small plant sizes, the limited availability of vegetation plot data has caused large uncertainties in fine‐grained mapping of species diversity. Here we used vegetation survey data from 1,609 field sites (>4,000 plots of 1 m2), remotely sensed data (ecosystem productivity and phenology, habitat heterogeneity, functional traits and spectral diversity), and abiotic data (water‐ and energy‐related, characterizing climate‐dominated environment) together with machine learning and spatial autoregressive models to predict and map grassland species richness per 100 m2 across the Mongolian Plateau at 500 m resolution. Combining all variables yielded a predictive accuracy of 69% compared with 64% using remotely sensed variables or 65% using abiotic variables alone. Among remotely sensed variables, functional traits showed the highest predictive power (55%) in species richness estimation, followed by productivity and phenology (48%), spectral diversity (48%) and habitat heterogeneity (48%). When considering spatial autocorrelation, remotely sensed variables explained 52% and abiotic variables explained 41%. Moreover, Remotely sensed variables provided better prediction at smaller grain size (<∼1,000 km), while water‐ and energy‐dominated macro‐environment variables were the most important drivers and dominated the effects of remotely sensed variables on diversity patterns at macro‐scale (>∼1,000 km). These findings indicate that while remotely sensed vegetation characteristics and climate‐dominated macro‐environment provide similar predictions for mapping grassland plant species richness, they offer complementary explanations across broad spatial scales.

Climate change and vegetation greening jointly drive the spatial pattern of net radiation variability in northern China

ABSTRACT The spatial and temporal variations of net surface radiation (Rn) are critical for comprehending ecological environments. Nonetheless, the intricate interplay among Rn dynamics, vegetation growth, climate, and natural factors remains inadequately elucidated. In this study, we estimated net surface radiation based on Landsat data and ERA5 meteorological data in the Google Earth Engine (GEE) platform, which closely matched the observable spatial distribution (R 2 = 0.96), with an average growth rate of 0.15 MJ m−2 mth−1. Trend analyses and spatial autocorrelation were used to explore the spatial and temporal changes in net radiation from 2000 to 2020, the global Moran's index for net radiation was found to exceed 0.76, with fluctuating increases, showing a highly positive spatial distribution of Rn. Local Moran's I predominantly fell into two categories: ‘High-High’ and ‘Low-Low’, with the first increasing in range and the latter decreasing. Combining GeoDetector and PLS-SEM analyses, temperature and vegetation emerge as predominant drivers of net surface radiation variation within the study area, each contributing more than 17% to Rn change. Furthermore, interactions between any two factors typically exhibits nonlinear enhancement. PLS-SEM underscores the influence of vegetation and climate on Rn, with other factors indirectly affecting net radiation changes by influencing vegetation growth.

Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021

Biodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground monitoring data, Landsat remote sensing, and environmental variables in the Three Rivers Headwater Region (TRHR) from 2000 to 2021. We established a reliable model for estimating grassland species diversity, analyzed the spatial and temporal patterns, trends of change, and the driving factors of changes in grassland species diversity over the past 22 years. Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R2 of 0.44 and an RMSE of 2.56 n/m2 on the test set. The spatial distribution of species diversity showed a pattern of abundance in the southeast and scarcity in the northwest. Trend analysis revealed that species diversity was increasing in 80.46% of the area, whereas 16.59% of the area exhibited a decreasing trend. The analysis of driving factors indicated that the changes in species diversity were driven by both climate change and human activities over the past 22 years in the study area, of which temperature was the most significant driving factor. This study effectively monitors grassland species diversity on a large scale, thereby supporting biodiversity monitoring and grassland resource management.

Chilling and Forcing Requirements of Wintersweet (Chimonanthus praecox L.) Flowering in China

Numerous studies have reported phenological changes and their driving mechanisms in spring flowering plants. However, there is little research on the shifts of winter flowering phenology and its response to forcing and chilling requirements. Based on the China Phenological Observation Network (CPON) ground observation data from nine sites over the past 20 years, we explored the spatial and temporal variation patterns of flowering plants and their response to chilling and forcing in wintersweet (Chimonanthus praecox L.), a common winter flowering plant species in temperate and subtropical zones of China. We used three chilling models (chilling hour, Utah, and dynamic models) and the growing degree hours (GDHs) model to calculate each site’s daily chilling and forcing. Using the partial least squares (PLSs) regression approach, we established the relationship between the first flowering date (FFD) and pre-season chilling and forcing in wintersweet, based on which we identified chilling and forcing periods and calculated chilling and forcing requirements. This study found that the FFD of wintersweet in China showed an overall advancement trend during the last 20 years. Still, there were temporal and spatial differences in the FFD of wintersweet among different sites. The PLS results showed that wintersweet also had periods of chilling and forcing, both of which co-regulated wintersweet flowering. We found the forcing and chilling requirements of wintersweet varied significantly from site to site. The higher the latitude is, the more chilling requirements are needed. The chilling requirements for wintersweet were about 6.9–34.9 Chill Portions (CPs) and 1.4–21.6 CP in the temperate and subtropical zones, respectively, with corresponding forcing requirements of 3.2–1922.9 GDH and 965.3–8482.6 GDH, respectively. In addition, we found that the temperature requirements of wintersweet were correlated by a negative exponential relationship, suggesting that chilling and forcing requirements have an antagonistic effect on initiating flowering phenology. Our results could help us understand how flowering dates of winter flowering plants respond to climate change.

Environmental and hydrological synergies shaping phytoplankton diversity in the Hetao irrigation district

Phytoplankton are crucial primary producers in freshwater ecosystems, driving matter and energy flow across trophic levels, essential for biodiversity and ecological balance. Most research emphasizes environmental factors shaping their diversity, while the role of hydrological connectivity remains poorly understood. This study collected 81 phytoplankton samples from the Hetao Irrigation District along a gradient from upstream to downstream and utilized high-throughput sequencing to evaluate the spatial distribution patterns of phytoplankton diversity. The study analyzed the impacts of environmental factors, hydrological connectivity (water surface ratio, Wp), and human activities (land-use intensity, LUI) on phytoplankton diversity. The results revealed that the phytoplankton community comprised 9 phyla, 158 families, 378 genera, and 1189 species. There were significant differences in phytoplankton diversity among different water bodies, with a gradual increase in phytoplankton diversity from west to east across the five major irrigation areas. Lake Ulansuhai had relatively low phytoplankton diversity. The ASV number, Chao1 index, and ACE index showed significant positive correlations with dissolved oxygen (DO), pH, and water temperature (WT). The Shannon index and Pielou'e evenness (Pielou_e) index showed significant positive correlations with the water surface ratio (Wp). The partial least squares model indicated that environmental factors directly influenced phytoplankton diversity. Hydrological connectivity indirectly affected phytoplankton diversity by altering environmental factors. We emphasize that hydrological connectivity is as important as environmental factors in driving phytoplankton diversity in the Hetao Irrigation District. This study provides key insights for water quality assessment and biodiversity conservation in the region.

Spatial patterns and driving factors of plant diversity along the urban-rural gradient in the context of urbanization in Zhengzhou, China.

Plant diversity is the basis for human survival and development, directly affecting the function and stability of urban ecosystems. Its distribution pattern and causes have been a central issue in ecological and landscape gardening research. Rapid urbanization in Zhengzhou City has led to the fragmentation of urban green spaces and damage to ecosystems, seriously affecting urban biodiversity conservation. Understanding the distribution pattern of plant diversity in the region and its relationship with environmental factors is crucial for maintaining and enhancing urban plant diversity. Plant data from 178 sample plots in the built-up area of Zhengzhou City were collected and combined with environmental factors, and the characteristics of plant diversity, richness patterns, and their main environmental explanations in Zhengzhou City were explored. Results showed that there were 596 plant species belonging to 357 genera and 110 families in the study area. There were five dominant families and four dominant genera. Four distinct spatial patterns of plant diversity were identified along the urban-rural gradient. Urbanization factors such as GDP per capita, house prices, and imperviousness within 500 m from the patch significantly influenced plant diversity. There was an imbalance between the spatial pattern of plant diversity and application of urban landscape greening in Zhengzhou City. Future studies should focus on the application of native plants, curb plant homogenization, and reduce anthropogenic interference, which are conducive to protecting and enhancing urban plant diversity. These results can provide a basis for understanding the distribution pattern and influence mechanism of urbanization factors on plant diversity and serve as a reference for policymakers and planners of plant diversity conservation in Zhengzhou City.