What is hidden in the distribution of sea cucumber faecal casts? Spatial point pattern analysis reveals tracemaker community competition in the Bering Sea abyssal plain

Interactions between the parasitic larvae of digenean trematodes (mainly gymnophallids) and bivalves often produce characteristic pit-like malformations on shells. Tracking these traces in past and modern marine death assemblages has provided valuable insights into parasite-host responses to natural and anthropogenic environmental changes. Despite major breakthroughs, empirical explorations of parasite-host dynamics in the geological record are primarily based on trace occurrence data, overlooking the ecological information embedded in the spatial distribution of these traces (e.g. infective behavior, association with specific host anatomy, spatial relationships of traces with different size classes). Spatial Point Pattern Analysis of Traces (SPPAT), increasingly used to study predatory traces on mollusks, offers a promising approach to address this gap. However, its application to trematode-host interactions requires careful consideration of assumptions and caveats, such as the minimum number of traces required to accurately capture parasite-host dynamics and the reliability of point patterns constructed from data across multiple host skeletons. Here, we present a spatially explicit framework for extracting information from spatial patterns of trematode-induced pits on bivalve shells using SPPAT. We address methodological questions involved in assembling a point pattern of traces from multiple host specimens, and discuss critical issues related to drawing inferences from pooled point data. Our approach is illustrated using Late-Holocene samples of Chamelea gallina from the Northern Adriatic Sea (Italy), a species of commercial importance and a key model in climate change research. Our findings indicate that trematode-induced traces on C. gallina are non-randomly distributed, forming aggregated patterns. Notably, we detect no significant spatial differences between the two size classes of traces retrieved. This study extends the methodological toolkit for analyzing parasite-host interactions and highlights the potential of spatial trace patterns to enhance our understanding of their ecological and temporal dynamics.

The clustering of creative industries in Mexico City: using spatial point pattern analysis to locate expected clustering locations

Size-dependent mortality of Khingan fir tree in old-growth forests: a spatial point pattern analysis

Sea pens are colonial octocorals inhabiting mostly muddy and sandy soft sediments, globally. Despite their cosmopolitan distribution, evidence of high functional value, and susceptibility to external stressors, sea pens remain poorly investigated relative to other coral species. Here, a quantitative analysis of the fine-scale spatial patterns of deep-sea sea pens (Octocorallia: Pennatuloidea) within a submarine canyon conservation area in English waters is conducted using Remotely Operated Vehicle (ROV) seabed images, three-dimensional photogrammetry models, and Spatial Point Pattern Analysis (SPPA). The density distribution of sea pens at the canyon flank scale (100s of metres) is also reported for context. At the canyon flank scale, sea pen density decreases with increasing depth, with colonies virtually absent from waters deeper than ca. 1100m. At the fine scale, sea pens exhibit a tendency to cluster relative to sea pens and tube-dwelling anemones (Cerianthidae sp.), suggesting mutual habitat associations. Conversely, overdispersion best characterises the nature of sea pen distribution relative to stalked sponges (Hyalonema sp.), suggesting occurrence of competition or, possibly, allelopathy. This study demonstrates that high-resolution spatial analysis techniques can be employed to disentangle biotic interactions among a set of taxa, revealing key processes and structures underlying the ecology of benthic fauna.

Understanding the drivers of spatial patterns in fossil communities is fundamental to paleoecology, yet direct evidence for biological mechanisms regulating interindividual spacing remains elusive. Brachiopod setae, hypothesized to function in feeding or defense, are exceedingly rare in the fossil record, especially among post-Cambrian taxa. Here, we present the report of exquisitely preserved setae from an exceptional in situ fossil assemblage of the early Silurian rhynchonelliform brachiopod Nucleospira calypta. Multiproxy analyses (scanning electron microscopy, X-ray fluorescence, and microcomputed tomography) revealed intricate ultrastructural details and diverse taphonomic pathways, leading to a reinterpretation of apparent calcitic preservation as primarily iron oxides with subsequent coating. Critically, the undisturbed nature of this aggregation allowed rigorous spatial point pattern analysis (Nearest-Neighbor Analysis, Thiessen polygons). This revealed a statistically significant, nonrandom, checkerboard-like distribution among individuals within the studied fossil deposit, indicative of active spacing regulation. Strikingly, the measured average interindividual spacing quantitatively relates to the length of the preserved setae. This provides the direct paleontological evidence demonstrating that these subtle morphological structures could have actively mediated spatial organization within a dense benthic community. Our findings illustrate a biological mechanism capable of shaping community structure, operating beyond passive environmental constraints or initial larval settlement preferences, and highlight the potential for subtle anatomical features to exert significant ecological influence in deep time.

Small-scale spatial structures of plant communities represent a critical driver of population dynamics, species interactions, and fundamental ecological processes. At localized scales, plants experience biotic and abiotic conditions that often deviate from broader environmental gradients. These conditions are shaped by conspecific and heterospecific neighborhood interactions, which directly influence individual fitness, survival, and regeneration. Small -scale spatial patterns arise from habitat heterogeneity, dispersal limitations, plant-plant facilitation or competition, interactions with biotic agents (e.g., pollinators, herbivores), and stochastic disturbances. Despite their ecological significance, a comprehensive synthesis of the mechanisms driving these spatial structures remains incomplete, hindering their integration into predictive models of community dynamics. This review synthesizes current knowledge to address critical gaps in understanding, focusing on six key determinants of small-scale spatial organization: (1) habitat heterogeneity, (2) plant dispersal mechanisms, (3) intra- and interspecific interactions, (4) biological environment, (5) disturbance regimes, and (6) multifactorial synergies. Additionally, the functional consequences of these spatial patterns are evaluated, particularly their roles in biodiversity maintenance, intraspecific genetic diversity, and ecosystem recovery trajectories. Methodological limitations are critically examined, including scale mismatches in spatial analyses, overreliance on equilibrium assumptions, and insufficient incorporation of plant functional traits. To address these challenges, several research priorities are proposed: (i) integrating small-scale spatial data into macroecological models using hierarchical frameworks, (ii) adopting plant-centric approaches to quantify individual-level interactions, (iii) identifying ecologically relevant spatial scales through gradient analysis, and (iv) employing spatially explicit models (e.g., point-pattern analysis, agent-based simulations) to disentangle stochastic and deterministic drivers. Finally, the translation of insights from small-scale spatial ecology into restoration practices is emphasized, advocating for leveraging pattern-process feedback loops and incorporating spatial heterogeneity into conservation frameworks. This approach aims to bridge the gap between theoretical understanding and practical applications in ecosystem management.

Several materials widely used in scientific research and industrial applications, including nano-filters and neuromorphic circuits, consist of fiber structures. Despite the fundamental structural similarity, the key feature that should be considered depends on the specific application. In the case of membranes and filters, the main concern has been on the pores among fibers, whereas in neuromorphic networks the main functionality is performed through the junctions of nanowires simulating neuron synapses for information dissemination. Precise metrological characterization of these structural features, along with methods for their effective control and replication, is essential for optimizing performance across various applications. This paper presents a comprehensive metrological framework for characterizing the spatial point patterns formed by pores or junctions within fibrous materials. The aim is to probe the influence of fiber randomness on both the point patterns of intersections (ppi) and pores (ppp). Our findings indicate a strong tendency of ppi toward aggregation, contrasting with a tendency of ppp toward periodicity and consequent pore uniformity. Both patterns are characterized by peculiarities related to collinearity effects on neighboring points that cannot be captured by the conventional anisotropy analysis of point patterns. To characterize local collinearity, we develop a method that counts the number of collinear triplets of nearest neighbor points in a pattern and designs an appropriate parameter to quantify them, also applied to scanning electron microscopy (SEM) images of membranes, demonstrating consistency with simulated data.

The study aims to analyze morphological variations and spatial distribution patterns of Krascheninnikovia compacta (Losinsk.) Grubov communities across 12 sampling areas at different elevations in the Tibetan antelope breeding grounds of the western Kunlun Mountains. Additionally, it projected the future climatically suitabie habitats of K. compacta under climate change scenarios, aiming to elucidate its community characteristics, spatial distribution dynamics, and the impacts of global warming on its growth. Integrated GIS, remote sensing, and unmanned aerial vehicles (UAVs) were used to investigate K. compacta communities. The Pearson correlation analysis revealed significant correlations between crown diameter, as well as between plant height and environmental factors. The redundancy analysis (RDA) results indicated that multiple environmental factors jointly explained the variation in plant height and crown diameter of K. compacta. Point pattern analysis, using the g(r) function combined with two null models, demonstrated changes in plant distribution during scale transitions. Additionally, the MaxEnt model was employed to project the potential suitable habitats of K. compacta under future climate scenarios. Overall, as the elevational gradient increases, the plant height of K. compacta gradually decreases while the crown diameter expands. Mean annual temperature (MAT) dominates the morphological variations in crown diameter and plant height, with lower temperatures correlating to shorter plant height and larger crown diameter. The complete spatial randomness (CSR) model indicates that across all elevations, the distribution patterns of plants transition sequentially from uniform to random, then clustered, and back to random as spatial scale increases. The heterogeneous Poisson (HP) model suggests that habitat heterogeneity is the primary driver of shifts in plant distribution patterns at larger scales. The MaxEnt model revealed distinct changes in suitable habitat areas of K. compacta under future climate scenarios. During 2061 to 2080s, its suitable habitats under the SSP126 and SSP585 pathways significantly contracted and expanded markedly, respectively.

Abstract The duality of liberal professional and gatekeeper of public registries is a common characteristic of the Latin notary in the 91 countries that use this legal figure. However, the optimal level of regulation to decrease restrictions without affecting the quality of service has yet to be explored. One of the little-studied factors in the literature is the geographic distribution of notaries in cities and the impact of this on service coverage. We thus analyzed the geographic location of Notaries Public and other gatekeepers in Mexico City with geographic information systems and point pattern analysis. We found that Notaries Public are spatially concentrated in areas around the financial center of the city, and far from highly populated areas, where their operating costs may not be covered. The clustering identified may be explained by the tendency for Notaries Public to establish themselves near their competitors to attract clients from neighboring notaries. As such, the law should consider public financing or the use of technological tools to ensure coverage of notarial services and reduce geographical disparities in low-income areas.

Introduction: Glioblastoma or GBM is an aggressive brain cancer with a 5 year survival rate below 10 percent. Glioma stem cells or GSCs drive GBM formation, growth, and resistance. Previously, analysis of 17,601 phase contrast images from 15 GBM patients revealed that neurodevelopmental GSCs form smaller, uniform clusters, while mesenchymal and injury-response GSCs exhibit complex, irregular growth patterns. This new study examines the spatial distribution and local cellular niche of GSCs along a neurodevelopmental gradient in primary GBM. We develop a geospatial map of GSCs in relation to other cell-types, identifying community patterns of organization. Methods: We profiled 14 primary, treatment-naive GBM tissue sections (5 micrometer thick) on a spatial transcriptomics platform (Xenium), utilizing a custom 414 gene panel to score the GSC, GBM and other cell signatures. We segmented cell boundaries using both Baysor and Proseg algorithms and performed hematoxylin-eosin (HE) staining of tissue sections post-Xenium run. We performed spatial clustering using SpaGCN and Banksy, followed by spatial distribution and spatial point pattern analysis (Ripley’s, Moran’s I). Results: Across the 14 tissues, we segmented 5, 705, 825 cells and annotated each cell with a cell-type label using pre-defined gene sets. This resulted in 15.2 percent GSC-like cells, 9.0% oligodendrocytic-OPC-like, 8.7% astrocytic-mesenchymal-like, 6.7% neuronal-like, 15.0% immune, 7.1% endothelial, and the rest of the cells showing mixed signatures. In total we identified 110-140 spatial clusters, which we classified into 8 domains by grouping highly correlated spatial clusters using Pearson correlation. All cell-types were differentially distributed both proportionally and spatially. Domains with highly correlated grouped clusters exhibited distinct spatial arrangements of cells as well. For instance, GSC-like cells were confined to unique spatial domains with specific geometric properties across cellular hierarchies. Likewise, immune and endothelial cells displayed enrichment in preferred domains, with some co-localized with GSC-enriched regions while others were devoid of these cell types. Therefore, we find that these geospatial topology maps could identify spatial enrichment patterns and distributional trends. Conclusions: This study deciphers GBM’s geospatial topology, where annotated cell-types are mapped to specific niches. Though the ultimate aim of the study will be to eliminate GSC-like cells in GBM, we posit that by revealing cell community patterns, we aim to shift therapeutic strategies from targeting single cells or molecules to biomarkers that address entire cell communities within which these GSCs are spatially and functionally connected to. This framework lays the groundwork for future biomarker discovery and therapeutic interventions in GBM. Citation Format: Shamini Ayyadhury, Fatemah Al Solaiman, Yuna Lee, Alyona Ivanova, Ana Nikolic, Farzaneh Aboulizadeh, Melanie Peralta, Patty Sachamitr, Michelle M. Kushida, Nicole I. Park, Fiona J. Coutinho, Owen Whitley, Panagiotis Prinos, Cheryl H. Arrowsmith, Sam Weiss, Sheila Mansouri, Gelareh Zadeh, Peter B. Dirks, Troy Ketela, H. Artee Luchman, Gary D. Bader, Trevor J. Pugh. Discovering the geospatial framework in glioblastoma: Uncovering hotspots for glioma stem cell hide-out and their therapeutic implications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 767.

Pollination success is influenced by factors such as density and distance from neighbouring conspecifics. However, the pure neighbourhood effects of spatial patterns of interaction on pollination success remains poorly understood. In this study, we used techniques of spatial point pattern analysis (SPPA) to investigate the relationship between the spatial distribution of a specialist pollinator, the weevil Derelomus chamaeropis, and the fruiting success of its host plant, the dwarf palm Chamaerops humilis, within a nursery pollination system. We georeferenced a dwarf palm population in a 22-hectare plot (96 individuals: 41 females/ 55 males), located at the Doñana National Park (SW Spain). We quantified the abundance and spatial pattern of adult weevil occupancy, and their correlation with the spatial distribution of dwarf palms. Additionally, we analysed the spatial pattern of fruiting success and how palm traits (number of inflorescences and flowers, and sex) influenced adult weevil abundance and fruiting success. Our findings revealed that presence/abundance of D. chamaeropis depended on plant sex, with female inflorescences showing significantly higher adult weevil abundances. We found a significant, negative density-dependent response, where higher neighbourhood density of palms led to reduced local weevil abundance. In contrast, we observed positive density and distance dependence for fruiting success at small spatial scales (2-5 m and 5-15 m), suggesting enhanced pollination success in dense patches. Our results indicate that weevil distribution is shaped by local resource availability and competitive interactions rather than broader palm arrangement. Additionally, pollination efficiency and fruit set benefit from positive distance-density dependence in dense patches.

ABSTRACTAimsUnderstanding the impacts of forest tree pathogens on understorey sapling populations is critical for understanding their population‐level effects and the likely successional trajectories of infected communities. We assessed the impacts of Phytophthora agathidicida, a soil‐borne pathogen, on the sapling population dynamics of the disease‐susceptible, locally dominant canopy tree, the conifer kauri (Agathis australis, Araucariaceae). We also examined the indirect effects of P. agathidicida on likely resistant allospecifics that span a range of shade‐tolerances as saplings, to predict future successional trajectories.LocationWaitākere Ranges, west of Auckland, Aotearoa‐New Zealand.MethodsWe analysed data from four kauri‐dominated permanent plots in Aotearoa‐New Zealand warm temperate rain forests. Two plots were early‐successional and two were late‐successional, one in each pair had overstorey kauri showing intense visual P. agathidicida symptoms, and the other overstorey kauri showing few symptoms. We examined the association between kauri trees and saplings using point pattern analysis and the relationship between the level of crowding around saplings and their growth and survival rates. We compared the growth and survival rates of kauri and allospecifics, categorised by shade tolerance, among the plots.ResultsKauri forms sapling banks under conspecific trees that were less dense in late‐successional forests and in those where the overstorey kauri showed symptoms of P. agathidicida infection. Despite having lower densities, kauri sapling growth rates were higher in symptomatic plots. The growth rates of light‐demanding allospecifics were also higher in these plots, with minor differences in mortality and growth rates for more shade‐tolerant allospecifics. P. agathidicida may promote sapling growth and survival of kauri and some allospecifics in infected plots.ConclusionsSapling vital rates and population sizes differed between asymptomatic and symptomatic plots, particularly in early‐successional settings, where P. agathidicida may reset succession in early‐successional communities back to those dominated by species that first colonised after disturbance.

Neoadjuvant therapy (NAT) is the standard of care for borderline-resectable and locally advanced pancreatic ductal adenocarcinoma (PDAC). It can be used to treat resectable PDAC. This study aimed to investigate how NAT remodels the tumor immune microenvironment (TIME) and whether this remodeling translates into survival benefits. We performed spatial and digital pathology analysis of 27 upfront resection patients (naïve group) and 39 age-, gender-, and stage-matched patients who had surgery after NAT (NAT group). AI-assisted digital pathology was used to annotate cancer cells and CD8 + T lymphocytes. Spatial correlation between CD8 + T lymphocytes and cancer cells for each case was assessed using spatial point pattern analysis, followed by generalized linear modeling (GLM) of quadrat counts of CD8 + T cells, with the quadrat counts of cancer cells as the independent variable. The regression coefficient was used to quantify the strength of their spatial correlation and then further assessed for association with patient survival. The analyses showed that the NAT group, compared with the naïve group, had increased spatial correlation of CD8 + T cells with cancer cells, suggesting enhanced effector T cell-cancer cell engagement in the NAT patients. Additionally, patients with a higher degree of spatial correlation between the two cells showed improved after-surgery survival. Through a new methodological framework that takes advantage of AI-assisted digital pathology and spatial point pattern analysis, our study has successfully captured the subtle effects of NAT-induced TIME remodeling and assessed its impact on prognosis of PDAC patients.

Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery in conjunction with topography and soil attribute data and employ a generalized cluster identification algorithm, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to study the underlying patterns of palms in two areas of Guyana, South America. The results of the HDBSCAN assessment were cross-validated with several point pattern analysis methods commonly used by ecologists (the quadrat test for complete spatial randomness, Morista Index, Ripley’s L-function, and the pair correlation function). A spatial logistic regression model was generated to understand the multivariate environmental influences driving the placement of cluster and outlier palms. Our results showed that palms are strongly clustered in the areas of interest and that the HDBSCAN’s clustering output correlates well with traditional analytical methods. The environmental factors influencing palm clusters or outliers, as determined by logistic regression, exhibit qualitative similarities to those identified in conventional ground-based palm surveys. These findings are promising for prospective research aiming to integrate remote flora identification techniques with traditional data collection studies.

BackgroundThe potential impact of urban structure, as population density and proximity to essential facilities, on spatial variability of infectious disease cases remains underexplored.AimTo analyse the spatial variation of COVID-19 case intensity in relation to population density and distance from urban facilities (as potential contagion hubs), by comparing Alpha and Omicron wave data representing periods of both enacted and lifted non-pharmaceutical interventions (NPIs) in Málaga.MethodsUsing spatial point pattern analysis, we examined COVID-19 cases in relation to population density, distance from hospitals, health centres, schools, markets, shopping malls, sports centres and nursing homes by non-parametric estimation of relative intensity dependence on these covariates. For statistical significance and effect size, we performed Berman Z1 tests and Areas Under Curves (AUC) for Receiver Operating Characteristic (ROC) curves.ResultsAfter accounting for population density, relative intensity of COVID-19 remained consistent in relation to distance from urban facilities across waves. Although non-parametric estimations of the relative intensity of cases showed fluctuations with distance from facilities, Berman’s Z1 tests were significant for health centres only (p < 0.032) when compared with complete spatial randomness. The AUC of ROC curves for population density was above 0.75 and ca 0.6 for all urban facilities.ConclusionResults reflect the difficulty in assessing facilities’ effect in propagating infectious disease, particularly in compact cities. Lack of evidence directly linking higher case intensity to proximity to urban facilities shows the need to clarify the role of urban structure and planning in shaping the spatial distribution of epidemics within cities.

ABSTRACT The Niger Delta region of Nigeria is a major oil-producing area which experiences frequent oil spills that severely impacts the local environment and communities. Effective environmental monitoring and management remain inadequate in this area due to negligence, slow response times following oil spills, and difficulties regarding access and safety. This study investigates the spatiotemporal patterns of oil spills along the pipeline network from 2013 to 2021 using geo-computation techniques. Utilising the spNetwork package in R, Network Kernel Density Estimates (NKDE) and its temporal extension, Temporal Network Kernel Density Estimates (TNKDE) were carried out. Pipeline data were transformed into 500-metre lixels (linear pixels) to compute network distances and generate density estimates. NKDE identified oil spill hotspots, while TNKDE illustrated the temporal transitions of spills. These methods surpass traditional approaches (e.g. KDE, cluster analysis, point pattern analysis) by incorporating network constraints and uncovering critical spatial–temporal patterns. The findings offer valuable insights for targeted interventions to reduce future spills and mitigate past impacts.

The mechanisms of soil preferential flow occurrence and development are influenced by topographic and geomorphological factors. However, the complex distribution patterns of soil preferential flow paths on different slopes remain unclear. Therefore, taking the subtropical evergreen broad-leaved forest area of Simian Mountain as the study area, this study conducted a quantitative analysis of the spatial distribution characteristics and spatial association of soil preferential flow at different slope positions (slope shoulder (SS), mid-slope (SM) and slope foot (SF)) using field staining tracer tests and spatial point pattern analysis. Results showed that the maximum infiltration depth, the depth of matrix flow, average dye coverage, preferential flow fraction and length index of the SM plot were all greater than those of the SS and SF plots. The number of preferential flow paths in different slope positions decreases with soil depth, with most preferential flow paths measuring 2.5-5.0 mm in each soil layer. The total number of preferential flow paths in SM plot was approximately twice that of the SS and SF plots, and the preferential flow paths in SM plot, regardless of pore size, were all in an aggregated distribution. These findings indicated that differences in soil physical properties and vegetation coverage at different slope positions lead to variations in the spatial distribution and association of soil preferential flow paths. Accordingly, strategies must be modified for the distribution, storage and utilisation of soil water resources. This study provides a theoretical basis for the effective management and protection of water resources in the Simian Mountain area and a scientific basis for exploring the mechanisms of the occurrence and development of the water cycle in forested watersheds.

Caragana tibetica is a common species in the shrub-encroached desert grasslands of Inner Mongolia, China. Studying its distribution and factors can improve our grasp of shrub-encroached grassland dynamics and aid in regional biodiversity conservation. This study examined eight C. tibetica communities using point pattern analysis to assess the spatial distribution pattern (SDP) and the influencing factors of C. tibetica scrubs. We also propose a new index, i.e., the degree of deviation index (DoDI), to quantify the SDP of scrubs. The results revealed the following: (1) The shrubland of C. tibetica in the study area showed aggregated distribution on the scale of 0–30 m. On the scale of 30–50 m, the degree of aggregation gradually weakened and random distribution appeared. (2) There was not a significant correlation between SDP and environmental factors; however, DoDI showed that habitat heterogeneity had a certain impact on C. tibetica in the study area. Our research indicates that spatial heterogeneity contributes to the SDP of shrub plants in the shrub-encroached grasslands of the Inner Mongolia Plateau, and the use of DoDI enhances the ability to quantify and isolate the role of spatial heterogeneity. This study helps to deepen the understanding of the mechanisms of shrub encroachment formation in grasslands.

Abstract Competitive interactions significantly impact plant diversity, playing a crucial role in community assembly. This study, conducted in the evergreen broadleaved forest of Bidoup-Nui Ba National Park in southern Vietnam, aimed to investigate neighborhood competition and spatial patterns of tree species. In 2024, a 4-ha study plot was established within the strictly protected zone of the national park for data collection purposes. A total of 7,666 trees with a diameter at breast height (DBH) ≥2.5 cm, representing sixty-six species from thirty-one families, were identified, measured for DBH, and mapped for stem location. Unmarked and marked point-pattern analyses were employed to examine the spatial patterns of the most abundant species. The results revealed that the fifteen most abundant species exhibited an aggregated distribution at small scales (0–30 m), with their spatial patterns correlating with their life-history stages. Specifically, the spatial arrangements shifted from aggregation during the juvenile stage to randomness or regularity at the subadult and adult stages. Intra- and interspecific competition were found to be closely tied to spatial scale, showcasing instances of both symmetric and asymmetric competition. Notably, interspecific associations mainly exhibited spatial independence, especially at 0–50 m scales. This study identified several vital ecological processes governing the spatial distribution and neighborhood competition among broadleaved tree species in the study area, including dispersal limitations, symmetric and asymmetric competition, and self-thinning of tree species.