Spatial Aggregation Patterns Research Articles

A novel amyloid MRI imaging based on analysis of texture features representing Aß aggregation

AbstractBackgroundThe importance of detecting amyloid β (Aβ) in the early stages of Alzheimer's disease has markedly increased following the approval of Lecanemab, a disease‐modifying drug. MRI is a non‐invasive and less expensive rather than amyloid PET as gold standard for Aβ biomarker, but its clinical ability to detect Aβ has not been demonstrated. MRI phase information reflects paramagnetic substance including iron associated with Aβ aggregation. We have developed a novel approach utilizing multi‐gradient echo (GRE) to perform texture analysis of the phase information, enabling visualization of Aβ. The primary aim of this study is to evaluate the diagnostic capabilities of this "Amyloid MRI" technique.MethodThe consecutive 48 participants (mean age: 72±10, mean MMSE: 25±4) who visited our memory clinic, and underwent both MRI and amyloid PET were enrolled. Amyloid PET images were visually interpreted as Aβ positive (n=30) or negative (n=18), and standardized uptake value ratio (SUVr) maps were generated. Multi‐GRE images were acquired with a 3‐Tesla MRI. We generated a phase slope map, i.e. a first‐order linear estimate of the phase change in time, and segmented four anatomical regions‐of‐interest (ROI) for cortex‐based analysis. Statistical analysis of texture features (mean, SD and entropy) derived from the phase slope map over each ROI was performed. We investigated the correlation between the mean value of regional SUVr and that of texture parameters of the phase slope map, and determined the optimal cut‐off of a linear classifier by ROC analysis based on the diagnosis of amyloid PET.ResultThe correlation coefficients between the mean value of SUVr and the texture features of the phase slope map in parietal, PCCpre, frontal and temporal were 0.57 (p<0.0001), 0.56 (p<0.0001), 0.48 (p<0.0006) and 0.22 (p<0.22), respectively. The optimal cut‐off values for diagnosing Aβ positive or negative showed a sensitivity of 83% (73‐93%), specificity of 89% (74‐103%) and accuracy of 85%(75‐96%).ConclusionThe texture features of the phase slope map could reflect the spatial aggregation pattern of Aβ. Our method highlighted the potential to visualize Aβ using MRI.

A spatiotemporal model for the effects of toxicants on the competitive dynamics of aquatic species

In this paper, we develop a reaction–diffusion model with negative toxicant–taxis that incorporates spatiotemporally inhomogeneous toxicant input to investigate the impact of toxicants on the competitive dynamics of two species in a polluted aquatic environment. Here the negative toxicant–taxis models the evasive movement of avoiding toxicants by species. We establish the global well-posedness of the model, analyze the existence and stability of spatially homogeneous steady states, and derive sufficient conditions for species extinction and coexistence. Through linear stability analysis, we identify sufficient conditions on model parameters that destabilize spatially homogeneous steady states under spatiotemporally uniform toxicant input. Numerical experiments reveal the influence of key toxicant-related factors (input rate, taxis intensity, and diffusivity) on competition outcomes and species distributions. Notably, strong negative toxicant–taxis can induce spatial aggregation and segregation patterns between the species and the toxicant under uniform toxicant input. Our findings suggest that toxicant–taxis may promote population persistence and coexistence, particularly when the toxicant input is not uniform in space and time and the toxicant does not diffuse fast (i.e. weak diffusivity). However, strong toxicant diffusion can diminish the impact of taxis, adversely affecting population persistence and species coexistence.

Revealing pest patterns: A comparative spatial distribution analysis of insect threats in Kharif rice across the key agricultural regions in the Eastern India.

This study investigates the spatial distribution and aggregation patterns of major insect pests in Kharif rice fields during the 2023 growing season in Eastern India. The analysis focuses on key pests such as yellow stem borer (YSB), gall midge, green leaf hopper (GLH), and brown planthopper (BPH), as well as rice thrips, caseworm, whorl maggot, Gundhi bug, grasshopper, and leaf folder. Using statistical indices, this study aims to understand pest behavior across Standard Meteorological Weeks (SMWs) to better inform pest control strategies. The study reveals significant clustering and aggregation patterns among the pests. YSB exhibited variance-mean ratio (VMR) values between 1.14 (40th SMW) and 1.96 (31st SMW), with dispersion parameter (K) values ranging from 0.57 to 21.65, and a peak index of dispersion of 132.66 (43rd SMW). Similarly, gall midge showed VMR values from 6.19 to 10.48, whereas GLH and BPH recorded VMR ranges of 1.19 to 132.10 and 1.01 to 1.50, respectively. These spatial distribution trends were confirmed through values for Iwao's patchiness index and Taylor's power law, indicating strong pest aggregation in specific areas. The results underscore the need for region-specific integrated pest management strategies that take into account pest clustering and environmental factors influencing pest distribution. Although an S-curve pattern of infestation-showing gradual population increases, rapid peaks, and eventual decline-was observed, the primary focus remains spatial patterns, which are critical for optimizing pest management and improving rice crop sustainability in the region. © 2024 Society of Chemical Industry.

Statistical and density-based clustering of geographical flows for crowd movement patterns recognition

With the rapid development of sensors and communication technologies, it has become easy to collect large-scale and long-term crowd movement positioning data, which brings new opportunities for studying crowd movement patterns. This paper proposes a novel Statistical and Density-Based Clustering algorithm (SDBC) to identify implicit significant spatial aggregation patterns in geographical flow data. Unlike existing flow clustering algorithms, this method identifies the hot spots of origin-destination (OD) flows based on local spatial statistics and density-growing clustering. It also evaluates the significance of the identified geographic flow clusters, effectively reducing the identification of spurious clusters generated by chance in data. In our method, the spatial neighborhood of each flow is first obtained based on spatial proximity, temporal similarity, and directional similarity. Then, the number of flows in the spatial neighborhood of each flow is calculated and used as the density measure. Based on this, high-density flows are automatically detected using local spatial aggregation statistics, and a hierarchical density-based clustering strategy is developed to merge adjacent high-density flows to generate candidate flow clusters. Finally, we perform permutation tests to infer the statistical significance of each flow cluster and eliminate the candidate clusters generated by chance. Experiments on synthetic data and real-world taxi trajectory data were conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method can accurately identify the statistically significant flow clusters of different shapes and densities and performs better than the available state-of-the-art flow clustering algorithms.

Spatial Distribution Characteristics and Sustainable Inheritance Strategies of National Traditional Fine Arts Intangible Cultural Heritage in China

A total of 417 intangible cultural heritage objects (ICHOs) are intertwined with traditional Chinese art, showcasing China’s rich historical heritage and distinctive creative allure. However, ICHOs currently grapple with a significant succession quandary due to modernization endeavors and the impacts of globalization. Hence, this study scrutinizes the spatial distribution of ICHO projects and inheritors and examines methodologies for inheriting these 417 ICHOs, employing approaches such as the closest neighbor index, Moran’s I index, kernel density estimation, geographic concentration analysis, and imbalance index assessment. Research indicates that ICHOs exhibit a spatial aggregation pattern, yet there is no substantial spatial correlation observed in their distribution. The national distribution highlights two core density zones: Beijing and Shanghai. In China, ICHO programs display uneven distribution across various types, levels, regions, and cultural zones. Regarding the ethnic composition of projects and inheritors, ICHO projects predominantly focus on the Han ethnic group, with a scattering of minority representations. Minority ICHO projects and inheritors are concentrated in central and eastern areas, while Han ICHO projects and inheritors are concentrated in central and western regions. Among the 148 ICHO projects lacking inheritors, 203 have one inheritor, and only 66 have several inheritors. Shanghai serves as the core density zone for 269 ICHO projects with inheritors, while Beijing holds that status for 148 ICHO projects without inheritors. Out of the 148 ICHO projects lacking inheritors, 115 belong to the Han ethnic group, with 33 split among 16 different ethnic minorities, and 2 have no inheritors. These disparities underscore the uneven distribution of ICHO projects and the critical issue of inheritance. This study identified education, tourism, digital communication, incentivizing inheritors, and international cooperation as potential strategies for preserving ICHA. Moreover, a sustainable inheritance pathway integrating government, education, tourism, and media is imperative for the effective perpetuation of ICHOs’ legacy.

Prevalence and distribution of transmissible venereal tumor in dogs from a rural community of Mexico

Objective: The aim of this work was to determine the rates of prevalence and recurrence of TVT infection in owned dogs, as well as the distribution of the disease, in Cuajinicuilapa, Guerrero, Mexico. Methods: This work was an observational, descriptive, longitudinal study. A TVT diagnosis was performed in all dogs in the community based on clinical records, a physical examination, and a cytological evaluation, and further confirmed by histopathology. Tissue samples were surgically obtained for histopathological analysis and, when required, to remove tumors and give treatment with vincristine; a GPS system was used to identify possible spatial groupings. All cases were re-evaluated one year after. Results: A prevalence of 5.15% was found in 1047 dogs (512 females and 535 males; 25 positive females and 29 positive males). TVT infection was more frequent in 2–3-years-old dogs. A spatial aggregation pattern was observed in the local cases. One year after, no lesions were found in the 29 animals treated. Conclusion: TVT is a common disease, and its distribution suggests its aggregation in certain areas of the locality. Further studies on the dynamics of dog populations in small cities are required.

Spatial multi-criteria analysis for prioritising forest management zones to prevent large forest fires in Catalonia (NE Spain)

Large forest fires are one of the most common environmental issues in the Mediterranean region. In this study, we defined a set of indicators and criteria based on available spatial data to assess and prioritize management zones for preventing large forest fires in Catalonia (NE Spain). In total, 22 indicators were defined and grouped into 5 criteria. The process involved geospatial modelling using a Geographical Information System (GIS) and the Multi-Attribute Utility Theory (MAUT) analysis. The conducted MAUT analysis resulted in the development of rules for a more standardized assessment of priority management areas. This assessment is based on participatory evaluation and introduces a utility-function-based hierarchical spatial model. Within the model, each of the 5 criteria contains relevant information, including fuel hazard, potential fire behaviour, fire extinction accessibility, valuable resources at risk, and historical fire data, serving as the foundation for a comprehensive analysis of fire prevention measures. During the assessment, two distinct sets of weights were applied: one based on expert evaluation and another using equal weights. These weights describe the relative importance of the indicators within each criterion in the assessment of fire prevention measures. Our results reveal noticeable differences between the two applied approaches. In general terms, management priority levels showcase certain spatial aggregation patterns and spatial polarization characteristics. Our study underscores the importance of participatory planning for prioritizing forest management areas to reduce fire impacts and presents a methodological framework that facilitates such spatial assessments, utilizing tools that combine expert knowledge and scientific expertise. Therefore, the main focus of this study is not on the obtained results, but on the novel methodological framework used, which consists in combining geospatial modelling with MAUT and the participatory workshops for generating the utility rules. This framework allows participation of stakeholders which at the same time enlarges credibility and acceptance of the methodology and results.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model

BackgroundMultidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). MethodsWe developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FindingsWe found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. InterpretationOur results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option.

Mechanism analysis of vegetation phenology in an urban agglomeration in an arid zone driven by seasonal land surface temperatures

In arid areas, vegetation is the habitat that sustains the life and survival of species, and it also plays a vital role in soil erosion prevention. This paper investigates the spatial aggregation patterns and trends of vegetation phenology (VP) and land surface temperature (LST) from 2002 to 2021. Additionally, the spatial Durbin model was used to analyze seasonal elements such as air temperature, evaporation, LST, precipitation, soil moisture, aerosol optical depth, and the impact of DEM on VP. Finally, explore the relationship between seasonal LST and urbanization and VP. The results show that in arid areas, the vegetation start of season time (SOS) showed an advancing trend, the vegetation end of season time (EOS) and length of season time (LOS) showed an extending trend, and the vegetation time of middle of season (MOS) tended to be stable. VP changes in oasis areas fluctuated significantly, while VP varies in non-oasis regions were stable. Under the scenario of spatial heterogeneity, LST is the most influential factor in VP. Spring LST has the greatest impact on SOS, winter LST on EOS and LOS. Urbanization advances SOS and extends EOS but has less impact on LOS. This study's results are essential for understanding the complex mechanism of VP changes in oases in arid regions and analyzing the distribution characteristics of VP under urbanization levels.

Construction of an Ecological Security Pattern in Rapidly Urbanizing Areas Based on Ecosystem Sustainability, Stability, and Integrity

The escalating pace of urbanization and human activities presents formidable challenges to landuse patterns and ecological environments. Achieving a harmonious coexistence between humans and nature of high quality has emerged as a global imperative. Constructing an ecological security pattern has become an essential approach to mitigating the adverse ecological impacts of urban sprawl, safeguarding human well-being, and promoting the healthy development of ecosystems. Focusing on ecosystem sustainability, stability, and integrity, this study constructed the ecological security pattern in rapidly urbanizing areas, emphasizing achieving a well-balanced integration of urban expansion and ecological preservation. Ecological sources were identified by an evaluation system of “ecosystem service function–ecological sensitivity–landscape connectivity”. Resistance surfaces were constructed by integrating natural and human factors. Ecological corridors and nodes were extracted by methods such as the minimum cumulative resistance and gravity models. Taking Nanchang City as an example, the results show that there were 15 ecological sources, primarily woodland, displaying a distinct “island” phenomenon. Additionally, there were 41 ecological corridors with a combined length of 2170.54 km, exhibiting a dense distribution in the southwest and a sparse distribution in the northeast. The city was found to encompass 122 ecological nodes, predominantly situated along the corridors near the ecological sources, indicating a strong spatial aggregation pattern. An optimized ecological security pattern of “one ring, two belts, three zones, and multiple nodes” was proposed for synergizing ecological protection, restoration, and rapid urbanizing.

Landscape Ecological Risk Assessment and Spatial Pattern Evolution Analysis of the Central Yunnan Urban Agglomeration from 1995 to 2020 Based on Land Use/Cover Change

The central Yunnan urban agglomeration represents a typical urban cluster in the southwestern region of China. The swift urbanization and land use changes in this region pose a severe threat to the ecosystem. A thorough assessment of the landscape ecological risk in the central Yunnan urban agglomeration holds paramount importance for devising effective risk management strategies and sustainable, high-quality development plans. This study utilizes long-term land-use raster data for six time periods (1995, 2000, 2005, 2010, 2015, and 2020) in the Central Yunnan urban agglomeration. Using GIS technology, a landscape risk index model is constructed, and a comprehensive assessment of landscape ecological risks in the Central Yunnan urban agglomeration is conducted using the 5 km × 5 km grid analysis method and Kriging interpolation. The results indicate that, between 1995 and 2020, the Central Yunnan urban agglomeration was dominated by forest land, grassland, and cultivated land as the primary land-use types. Forest land covered over 48% of the total area, while grassland and cultivated land accounted for more than 26% and 18%, respectively. Notably, construction land underwent a significant increase, mainly due to conversions from cultivated land, forest land, and grassland. Over a span of 25 years, the study area has experienced a continual rise in landscape ecological risk. The landscape ecological risk was mainly characterized by medium, higher, and high ecological risk. Grassland predominated in areas with medium levels of ecological risk, while cultivated land and construction land were predominant in regions with higher and high levels of ecological risk. Spatially, regions with lower ecological risk were primarily distributed in the Chuxiong Yi Autonomous Prefecture, whereas areas with higher and high levels of ecological risk were concentrated in Qujing City and Kunming City. The spatial aggregation patterns of landscape ecological risk in the Central Yunnan urban agglomeration featured “high–high” (H–H) and “low–low” (L–L) clusters, both displaying an initial increase followed by a decrease. The primary factors contributing to the rise in the landscape ecological risk index were identified as urban expansion, population growth, ecological fragmentation, and vegetation destruction. The study’s outcomes can offer valuable insights for optimizing land resources and promoting sustainable development in the Central Yunnan urban agglomeration.

Hotspots and Factors Influencing Vertebrate Roadkill on the Ring Changbai Mountain Scenic Road, China

The spatial aggregation patterns of wildlife-vehicle collisions are used to inform where mitigation measures are most needed. Based on 10 years of observations of vertebrate roadkill on the Ring Changbai Mountain Scenic Road, the spatial distribution characteristics of roadkill incidence were analyzed in this study. Using the field survey method to investigate roadkill incidents and their influencing factors, we applied generalized linear mixed modeling (GLMM) for model selection and constructed roadkill models for different taxa groups. The spatial distribution patterns of roadkill hotspots vary among different taxa and exhibit a unimodal or multimodal distribution. The road section along a river and with a minimal distance between the road and the water has a high incidence of roadkill. The density of roadkill for various taxa decreases as the distances from rivers, bridges, and ponds increases. However, there appears to be no correlation between the density of bird roadkill and any factors. Finally, wildlife crossing facilities and guidance measures aimed at reducing the incidence of roadkill and enhancing the selection of routes and wildlife crossing structures in the area are formulated.

Spatial-temporal dynamics of land use carbon emissions and drivers in 20 urban agglomerations in China from 1990 to 2019.

Urban agglomerations (UAs) are the largest carbon emitters; thus, the emissions must be controlled to achieve carbon peak and carbon neutrality. We use long time series land-use and energy consumption data to estimate the carbon emissions in UAs. The standard deviational ellipse (SDE) and spatial autocorrelation analysis are used to reveal the spatiotemporal evolution of carbon emissions, and the geodetector, geographically and temporally weighted regression (GTWR), and boosted regression trees (BRTs) are used to analyze the driving factors. The results show the following: (1) Construction land and forest land are the main carbon sources and sinks, accounting for 93% and 94% of the total carbon sources and sinks, respectively. (2) The total carbon emissions of different UAs differ substantially, showing a spatial pattern of high emissions in the east and north and low emissions in the west and south. The carbon emissions of all UAs increase over time, with faster growth in UAs with lower carbon emissions. (3) The center of gravity of carbon emissions shifts to the south (except for North China, where it shifts to the west), and carbon emissions in UAs show a positive spatial correlation, with a predominantly high-high and low-low spatial aggregation pattern. (4) Population, GDP, and the annual number of cabs are the main factors influencing carbon emissions in most UAs, whereas other factors show significant differences. Most exhibit an increasing trend over time in their impact on carbon emissions. In general, China still faces substantial challenges in achieving the dual carbon goal. The carbon control measures of different UAs should be targeted in terms of energy utilization, green and low-carbon production, and consumption modes to achieve the low-carbon and green development goals of the United Nations' sustainable cities and beautiful China's urban construction as soon as possible.

Spatial aggregation patterns in four mistletoe species: ecological and environmental determinants.

Plant spatial distribution is an important topic in ecology as it determines species coexistence and biodiversity dynamics. Usually, plants show clustered distributions in nature. Mistletoes are a good example of aggregated distributions, as they form dense aggregations due to several factors (availability of competent hosts, seed dispersal vectors, microclimate conditions). We analysed four native mistletoe species with divergent life histories and host ranges: Desmaria mutabilis and Tristerix corymbosus from the temperate rainforests of southern Chile; and Tristerix aphyllus and Tristerix verticillatus from the northern semi-desert zone. While T. corymbosus and T. verticillatus have a wide host range, T. aphyllus and D. mutabilis are specialists that can parasitize only a few plant species. We hypothesized that specialized species would be more aggregated due to ecological and environmental restrictions. We used heterogeneous Poisson models to quantify spatial aggregation. Three of the four mistletoe species were spatially clustered at both environments, with aggregation being stronger in the temperate rainforest of southern Chile and particularly in the host-specialist species. Our results suggest that environmental constraints are more important than ecological constraints (host range) in shaping mistletoe spatial structure. Mistletoe aggregated spatial distribution depends primarily on the environment that they inhabit, which conditions host spatial availability, and arrangement.

Assessment of the spatial association between multiple pollutants of surface water and digestive cancer incidence in China: A novel application of spatial machine learning

Surface water pollution and digestive cancers are concerning environmental health issues. Multiple pollutants of surface water coexist within unique geospatial heterogeneous contexts of surface water flow, which can lead to complex patterns of impact on cancer incidence. This complexity has posed challenges to comprehensive assessment. In this study, we integrated nationwide surface water data of 21 pollutants collected from 3632 sections of nine large river basins and cancer incidence data for six digestive cancers covering 381.6 million people in China. Local Moran's I method was used to assess the patterns of spatial aggregation of each surface water pollutant separately to observe the local spatial nonstationarity and heterogeneity of the pollutant concentrations in different spatial areas. A geographically weighted random forest (GWRF) model, a spatial machine learning approach, was applied to address the spatial heterogeneity of the pollutants and assess their association with cancer incidence. Goodness-of-fit and overall performance of the GWRF model were assessed by the local statistics in the out-of-bag set, such as the mean local pseudo-R2, increase in mean squared error, and local residuals in each local model. Compared with the aspatial random forest model, the GWRF model had better performance (mean local pseudo-R2 ranged from 0.16 (gallbladder cancer) to 0.60 (pancreatic cancer)). Surface water pollution was associated with digestive cancer incidence mainly in the Songhua and Liaohe, Haihe, and Huaihe River Basins. By focusing on these areas, we identified key pollutants specific to different cancer types. Incidence of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancer was associated with common pollutants such as fluoride and arsenic. Our assessment provides targets for the government and environmental health specialists to take tailored actions to control pollution and effectively prevent cancer incidence.

Spatial Statistical Analysis and Risk Factor Identification of COVID-19 in China

Objectives: In this paper, we discuss the spatial aggregation and evolution of COVID-19 in China and identify the risk factors affecting the spread of the disease. The aim is to provide insights that can be used to implement timely and effective interventions in the face of similar infectious diseases in the future and to ensure the safety of people around the world. Methods: We used spatial statistics and measurement methods to analyze the spatial aggregation and evolution of COVID-19 in China. We carried out spatial visualization mapping and spatial statistical analysis on the data of the epidemic. Various risk factors of COVID-19 spread at the provincial level in China were comprehensively discussed by combining geographic detector and spatial Dubin model. Results: The analysis revealed the spatial aggregation and evolution patterns of COVID-19 in China and the risk factors affecting the spread of the disease, including population density, transportation network, and climate factors. The geographic detector and spatial Dubin model were effective in identifying the risk factors, and the results provide valuable insights for implementing timely and effective interventions. Conclusions: We emphasize the importance of timely and effective interventions in the face of infectious diseases such as COVID-19. Our results can raise awareness of prevention and control and respond to potential outbreaks of similar infectious diseases in the future. The study provides a deep understanding of COVID-19 and its spatial patterns, and the insights gained can safeguard both lives and property worldwide.

Analyzing the impacts of topographic factors and land cover characteristics on waterlogging events in urban functional zones

Rapid urbanization and climate changes result in frequent occurrence of urban waterlogging disasters, which cause serious economic damage and pose a threat to residents' safety. Understanding the spatial characteristic and the key influencing factors of urban waterlogging has significant implications for mitigating waterlogging. In this study, the officially issued representative waterlogging points were obtained, as well as the topographic factors and land cover characteristics were selected to compare their impacts on the waterlogging event density in a highly urbanized area at urban functional zone (UFZ) scale, and to quantify the contributions of the key influencing factors on urban waterlogging events. Results showed the average density of urban waterlogging events in the study area is 9.2 points/km2, and 38.4 % of the waterlogging events are distributed in REZ. The distribution of waterlogging points in the study area revealed a significant multi-core and multilevel spatial aggregation pattern, and 12.1 % of the study area was high-density waterlogging area. In the total UFZs, the correlation coefficients of topographic indices with waterlogging density were relatively weaker than the other land cover characteristic metrics. The impervious surface ratio had significant contributions in all UFZ types. The larger ratio of impervious surface significantly increased the density of waterlogging events. The increase in the ratio of green space can effectively decrease the density of urban waterlogging. In the total UFZs, the top 3 key influencing factors of urban waterlogging were PR (35.9 %), COHESION (32.5 %) and DIVISION (11.8 %). The higher connectivity of landscape patches in REZ, INZ and COZ, as well as the increase of landscape dispersion or diversity in REZ, EGZ, INZ and GSZ can effectively reduce the occurrence of urban waterlogging. This study provides a better understanding of the formation mechanism of urban waterlogging disasters and potential implications for prioritized waterlogging mitigation strategies.

Within-field spatial patterns of Helicoverpa zea (Lepidoptera: Noctuidae) and spatial associations with stink bugs and their injury in field corn.

The corn earworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae), is a cosmopolitan pest in the field crop landscape in the southeastern United States. Field corn (Zea mays L.) is the most important midseason host for H. zea where intensive selection pressure occurs for resistance to insecticidal toxins from Bacillus thuringiensis (Bt). Because spatial patterns of H. zea in field corn have not been extensively studied, field corn was sampled for H. zea larvae and injury in 2021 and 2022. Patterns of spatial aggregation were identified in a number of fields in both larval populations and injury. Aggregation of H. zea larvae was less common at R5 than at R2. Associations between the spatial patterns of H. zea and the variability in crop phenology were identified in some fields, with positive associations between plant height and H. zea larvae, indicating that ovipositing H. zea moths avoid areas with reduced plant height and delayed reproductive maturity. Additionally, negative spatial associations between stink bug ear injury and H. zea larvae and their injury were found in a small number of cases, indicating some spatial interactions between the two pest complexes and their injury. Results from these studies provide valuable insight into the spatial patterns of H. zea in field corn. An understanding of the local dispersal and population dynamics of H. zea can be used to help further improve integrated pest management and insect resistance management programs for this major polyphagous pest.

Spatiotemporal dynamics of a palm weevils' outbreak and susceptibility of peach palm (Bactris gasipaes Kunth) landraces in a germplasm bank in southwestern Colombia

AbstractThe palm weevils Rhynchophorus palmarum and Dynamis borassi are prominent pest species in peach palms and coconut in Colombia. Nevertheless, knowledge of the population dynamics of these weevil species in the neotropical region is still scarce. In this study, we systematically sampled these weevils over 2 years using pheromone traps (1 trap ha−1) in an experimental peach palm plantation under heavy weevil infestation in southwestern Colombia. Additionally, we censused the palms in a portion of the plantation (a peach palm germplasm bank) before and after the weevil monitoring. We analysed the data to elucidate sex‐specific temporal and spatial patterns of both R. palmarum and D. borassi in a 19.13 ha area. Furthermore, we sought to detect peach palm landraces within the germplasm bank susceptible and resistant/tolerant to the weevil attack. During periods when insect populations were high, we observed that R. palmarum consistently outnumbered D. borassi. Specifically, we found that R. palmarum reached up to 80 insects trap−1 month−1, while D. borassi only reached around 30 insects trap−1 month−1 during the peak months. However, both species exhibited synchronous population peaks, which were not related to rainfall or temperature but rather to the phenological stage of the peach palm. Furthermore, the weevils had a highly aggregated spatial pattern; insect catchments were more prolific in areas where the landraces Nariño and Risaralda were sown. Moreover, the census evidenced that these two landraces were the most affected. Contrarily, weevil capture was overall low around the Guaviare landrace, this was the only landrace that exhibited an increase in stipes between census years. Our results highlight that complementary control strategies are needed to counteract sudden weevil population outbreaks, as pheromone traps are a good tool for monitoring but not for effective control. Furthermore, more importantly, a peach palm breeding programme based on the identified genotypes should be the basis for the integrated pest management of palm weevils.

Global scale coupling of pyromes and fire regimes

Different interpretations of the fire regime concept have limited the capacity to allocate specific fire regimes worldwide. To solve this limitation, in this study, we present a framework to frame contemporary fire regimes spatially on a global scale. We process historical wildfire records between 2000 and 2018 across the six continents. We uncover 15 global pyromes with clear differences in fire-related metrics, such as frequency and size. The pyromes were further divided into 62 regimes based on spatial aggregation patterns. This spatial framing of contemporary fire regimes allows for an interpretation of how a combination of driving factors such as vegetation, climate, and demographic features can result in a specific fire regime. To the best of our knowledge, this open source platform at unprecedented scale expands on existing classification efforts and bridges the gaps between global and regional fire studies.