Spatial Links Research Articles

Genesis of the Haopinggou breccia-hosted Au deposit, western Henan Province, China

Breccia-hosted Au deposits within the North China Craton (NCC) exhibit a complex genesis, with some breccias being genetically linked to Au mineralization and others unrelated. Understanding the origin of these breccias is crucial for elucidating the genesis of associated ore deposits and guiding exploration strategies. The Haopinggou deposit in the Xiong’ershan district, on the southern margin of the NCC, comprises quartz-carbonate vein-hosted Ag–Pb–Zn–Au deposits and related breccia pipe in metamorphic rocks of the late Archean to early Paleoproterozoic Taihua Group. The origin of the Haopinggou breccia is crucial to understand the Xiayu Ag-Pb-Zn-Au mineralization (5640 t Ag). In this study, we examine the genetic relationship between breccia and Ag‒Pb‒Zn‒Au mineralization, drawing on new insights from the characteristics of the Haopinggou deposit, breccia features, breccia‒hosted Au mineralization, and isotopic data. These findings enhance our understanding of the Haopinggou Ag‒Pb‒Zn‒Au hydrothermal systems and refine exploration models for discordant breccia pipes. Recent underground tunnel surveys and drill-core logging reveal a close spatial link between the breccia and hidden granite porphyry. Juvenile magmatic clasts (granite porphyry) and cement (K-feldspar and quartz crystallized from granite porphyry) are present in the breccia pipe above a granite porphyry apophysis, indicating that the breccia was formed by phreatomagmatic brecciation caused by emplacement of the granite porphyry. Fluidization at the cm- to 100-m-scale reveals that the movement direction of clasts inside the breccia pipe was from northwest to southeast after crypto-explosion, creating a northwest-trending breccia zone. Anhedral monazite (131.2 ± 7.6 Ma) encapsulated in pyrite from the breccia constrains the upper limit of brecciation. The δ34S values of pyrite in rock flour and hydrothermal cements range from 3.8 ‰ to 5.3 ‰ (mean: 4.8 ‰) and 2.7 ‰ to 7.5 ‰ (mean: 5.2 ‰), respectively, with δ34S-sphalerite in hydrothermal cement ranging from 5.7 ‰ to 6.1 ‰ (mean: 5.9 ‰) and δ34S-chalcopyrite ranging from − 0.9 ‰ to 5.6 ‰ (mean: 2.9 ‰). The S isotope characteristics of vein-hosted and breccia-hosted mineralization zones are consistent, with complete alignment of the sulfide assemblages and alteration features. These pieces of evidence collectively indicate that both vein- and breccia-hosted mineralization zones are products of magmatic activity. The metallogenic model for the Haopinggou breccia pipe underscores its unique formation mechanism, driven by pre-existing northwest-striking structural weaknesses and fluidization. This model implies a predominant northwest‒to‒southeast transport of clasts and raises the hypothesis of an underlying porphyry‒type Au mineralization system at depth.

Is Working Memory Necessary for Both Selection and Calculation in Level 1 Visual Perspective Taking? Evidence from Children and Adults.

Previous research has suggested that there are two modular processes of perspective selection and calculation in level 1 perspective taking. Evidence further showed that verbal working memory is associated with both processes in adults. However, research has not tested whether verbal working memory may be associated with working memory in children. Moreover, since perspective taking is associated with spatial working memory, it is necessary to investigate whether spatial working memory links to both processes. By recruiting 9-year-old children and college students in the single-task paradigm and the dual-task paradigm, we conducted two experiments to answer these questions. Results in experiment 1 suggested that verbal working memory correlated with adults' perspective calculation, but with both processes in children's perspective taking. Results in experiment 2 showed that spatial working memory is associated with adults' perspective calculation and children's perspective selection. These findings suggest that different components of working memory play distinct roles in the processing of perspective taking, which is moderated by age.

Petrogenesis of Oligocene Granitoids and its Implications on Beryllium Mineralization at Pagele, Nianqingtanggula Mountain (Central Tibet)

AbstractThe newly discovered Oligocene granitoids (33.1–28.7 Ma) at Pagele are magmatic rocks related to beryllium mineralization during the India–Asia late‐collisional stage. This discovery provides an ideal example to study the late‐collisional orogeny and beryllium prospecting in the Lhasa terrane. The Oligocene granitoids include porphyritic granodiorite, Stage I, II and III granites, and granitic pegmatite. Geochemical analysis shows that the porphyritic granodiorite is characterized by high SiO2, K2O, total ΣREE contents, and (La/Yb)N ratios; while the latter two by higher SiO2, lower ΣREE and (La/Yb)N ratios. Notably, the granitic pegmatite has extremely high Y/Ho, low K/Rb and Zr/Hf, and distinct REE tetrad effect (1.14–1.21). This study suggests that the porphyritic granodiorite may be derived from partial melting of beryllium‐rich materials composed of Lhasa ancient crust (70%–80%) and enriched Lhasa lithospheric mantle (20%–30%) under the tearing subduction of Indian slab. The three‐stage granites and granitic pegmatite, which contain higher beryllium contents or beryls, were likely generated by highly fractionation of the porphyritic granodioritic magma or other homologous magma. Considering the possible genetic and spatial link between Indian slab tear and rifts, we suggest that highly‐fractionated granites in rifting systems represent important Be prospecting targets in the Lhasa terrane.

Urban synergistic carbon emissions reduction research: A perspective on spatial complexity and link prediction

Reducing urban carbon emissions (UCEs) holds paramount importance for global sustainable development. However, the complexity of interactions among urban spatial units has impeded further research on UCEs. This study investigates synergistic emission reduction between cities by analyzing the spatial complexity within the UCEs network. The future potential for synergistic carbon emissions reduction is predicted by the link prediction algorithm. A case study conducted in the Pearl River Basin of China demonstrates that the UCEs network has a complex spatial structure, and the synergistic capacity of emission reduction among cities is enhanced. The core cities in the UCEs network, including Dongguan, Shenzhen, and Guangzhou, have spillover effects that contribute to synergistic emission reduction. Community detection reveals that the common characteristics associated with UCEs become concentrated, thereby enhancing the synergy of joint efforts between cities. The link prediction algorithm indicates a high probability of strengthened carbon emission connections in the Pearl River Delta, alongside those between upstream cities, which shows potential in forecasting synergistic emission reductions. Our research framework offers a comprehensive analysis for synergistic emission reduction from the spatial complexity of UCEs network and link prediction. It acts as a worthwhile reference for developing differentiated policies on synergistic emission reduction.

Phosphatidylethanolamines link ferroptosis and autophagy during appressorium formation of rice blast fungus.

A cell death pathway, ferroptosis, occurs in conidial cells and is critical for formation and function of the infection structure, the appressorium, in the rice blast fungus Magnaporthe oryzae. In this study, we identified an orthologous lysophosphatidic acid acyltransferase (Lpaat) acting at upstream of phosphatidylethanolamines (PEs) biosynthesis and which is required for such fungal ferroptosis and pathogenicity. Two PE species, DOPE and SLPE, that depend on Lpaat function for production were sufficient for induction of lipid peroxidation and the consequent ferroptosis, thus positively regulating fungal pathogenicity. On the other hand, both DOPE and SLPE positively regulated autophagy. Loss of the LPAAT gene led to a decrease in the lipidated form of the autophagy protein Atg8, which is probably responsible for the autophagy defect of the lpaatΔ mutant. GFP-Lpaat was mostly localized on the membrane of lipid droplets (LDs) that were stained by the fluorescent dye monodansylpentane (MDH), suggesting that LDs serve as a source of lipids for membrane PE biosynthesis and probably as a membrane source of autophagosome. Overall, our results reveal novel intracellular membrane-bound organelle dynamics based on Lpaat-mediated lipid metabolism, providing a temporal and spatial link of ferroptosis and autophagy.

Conditional room layout generation based on graph neural networks

In this paper, we present a novel end-to-end variational generative model that utilizes graph-based latent representations for indoor scene synthesis at one time. In contrast to prior research, our method deviates from the practice of gradually introducing and arranging furniture in an empty room using autoregression. Instead, it focuses on acquiring a comprehensive implicit representation of the room’s original architectural structure and the placement of furniture. We initially transform the 3D room scene into a dense scene graph, where nodes correspond to the objects present in the room while edges reflect the spatial location links and functional correlations between objects. Then, a neural network is trained to acquire the graph-based latent representation of the room scene through iterative message passing, ultimately resulting in the acquisition of the data distribution on the latent space of the room layout. Given the architectural structure of an empty room as a prerequisite for scene synthesis, the generative model has the ability to sample from the prior distribution of the room’s latent representation. This allows the model to then decode and generate a variety of room layouts. We evaluate our method with the state-of-the-art 3D indoor scene dataset and generation methods. The experimental results demonstrate that our method achieves more rational and diverse outcomes in the context of generating scenes under specific conditions.

Facial Recognition Using Convolutional Neural Network Using Real-Time Data

Recent years have seen the rise of facial recognition as a significant technological advancement, with several applications in the fields including security, surveillance, authentication systems, and Human-Computer Interface. Numerous sectors have undergone radical change as a result of theirability to automatically identify and validate people based on their facial traits, opening new doors for innovation. The main objective of facial recognition is to create automated systems that can correctly identify and validate people from pictures or videos. The limitations of traditional methods in capturing complex and discriminative facial patterns included the reliance on handmade features and shallow learning techniques. However, facial recognition has made great progress since the introduction of deep learning, more notably Convolutional Neural Networks(CNNs). CNNs are the perfect tool for capturing fine facial characteristics because they have demonstrated an amazing capacity for hierarchical representations that can be directly learned from unprocessed image data. In this paper, the authors focus on facial recognition using a CNN model, intending to improve the accuracy and resilience of this crucial technology. The authors have applied a well-built CNN modelto address the challenges of facial recognition. We utilize deep learning to automatically identify and extract high-level features from facial images, enabling more accurate and reliable identification. The CNN model's architecture was thoughtfully created to utilize the underlying spatial links and regional patterns visible in facial data. By utilizing a large number of convolutional and pooling layers, the model cansuccessfully capture both low-level qualities like edges and textures and high-level facial traits like facial landmarks and expressions

“Hip Hop and the Televisual Global South: Atlanta and Sintonia”

In recent years, audiences in the United States and around the globe have witnessed an explosion of television and streaming content focused on hip hop. Such content contemplates, historicizes, promotes, and represents the art form, artists, industry, and regional genres that have together emerged as a central feature of contemporary popular culture. This article explores the spatial and thematic links to the theoretical Global South in Atlanta and Sintonia. Analyzing the series’ respective portrayals of race and inequality from the margins, I examine how hip-hop/ funk and the Global South function as narrative tools and symbolic spaces through which notions of a shared precarity stemming from global flows of capital are constructed, consumed, and challenged. By considering the series’ connections to the Global South, this article contributes to a growing body of work that aims to move beyond research that centers the west and the nation-state.

The In-Ga-Sb association of the post-Variscan Zn-Pb-Ag vein deposit at Lautenthal, Upper Harz Mountains, Germany: sphalerite mineral chemistry

The Lautenthal sphalerite-galena vein deposit is part of the world-class Upper Harz Pb-Zn-Ag district in the Harz uplift block of the Paleozoic Variscan fold belt in Germany. Its sphalerite-dominated mineral association was studied using bulk-ore chemistry, electron probe microanalysis, and laser ablation-ICP-mass spectrometry. Gallium and locally In are the main high-tech-relevant trace elements hosted by sphalerite, with up to 150 ppm Ga and up to 380 ppm In in hand-picked sphalerite samples (mean In/Zn, 0.70 × 10−3). Ore concentrates (≤ 50 kg) contain up to 65 ppm Ga and up to 109 ppm In (mean In/Zn, 0.36 × 10−3). Accessory Fe-Co-rich gersdorffite-1 occurs in the earlier quartz-sulfide ore stage and Sb-rich gersdorffite-2 in the later carbonate-sulfide stage. Enrichment patterns of In are either defined by overprinting textures in the Fe-richer sphalerite-1 of the earlier stage, or relate to primary growth zoning in Fe-poor sphalerite-2 of the later stage. Using the sphalerite geothermometer GGIMFis, formation temperatures (median) of sphalerite-1 were estimated at ~ 230 °C for the Lautenthal orebody and at ~ 175 °C for the Bromberg orebody, which may indicate lateral T-zonation for the earlier ore stage. Sphalerite-2 data indicate formation temperatures of ~ 185 °C (median). Copper-bearing brines of the carbonate-sulfide stage with assumed temperatures of ~ 250 °C initiated replacement of In-poor sphalerite-1 by chalcopyrite and remobilization of Zn and trace elements. Indium-rich sphalerite-2 occurs associated with calcite and fine-grained galena. A direct spatial or temporal link of ore formation to a magmatic-hydrothermal system is unlikely, which contrasts to In-rich epithermal and tin-polymetallic vein deposits worldwide.

Spatial diversity-based FSO links under adverse weather conditions: performance analysis

Spatial diversity-based FSO links under adverse weather conditions: performance analysis

Regional Setting of Temples in Dakshina Kosala, India: Spatial Distribution and Connections

Introduction: The region of Dakshina Kosala (also known as South Kosala) is mentioned in various ancient Indian texts. The exact geographical boundary has continuously changed over time, and today, only a rough estimate can be made of the Dakshina Kosala region, which approximately covers the northern and central parts of the present-day state of Chhattisgarh, along with the western part of the state of Odisha, India. Excavations and writings show that this was a prosperous region near central India, without any seaport, but with large markets, trade centers, and educational institutions. To access this region, many trade routes were established from various parts of India. This led to the development of cities with markets along these trade routes. Many temples were also seen in this region, serving as examples of the intangible heritage of the place. Purpose of the study: This region made a significant contribution to the temple art and architecture of India. The present study aims to determine if there is a spatial and architectural link between the temples in this region. Methods: The methodology involved an initial phase of reading and analyzing pertinent literature to prepare detailed maps delineating the boundaries of Dakshina Kosala. Subsequently, employing rigorous analysis with Google Earth and GIS tools, ancient trade routes were traced, temples were located on the maps, and a comprehensive analysis was conducted to derive conclusive results. Results: This paper establishes a relationship between the geographical locations and construction materials of the temples within and outside the geographical boundary of Dakshina Kosala.

On the elephant trails: habitat suitability and connectivity for Asian elephants in eastern Indian landscape.

Identifying suitable habitats and conserving corridors are crucial to the long-term conservation of large and conflict-prone animals. Being a flagship species, survival of Asian elephants is threatened by human-induced mortality and habitat modification. We aimed to assess the habitat suitability and connectivity of the Asian elephant Elephas maximus Linnaeus, 1758 habitat in the state of Odisha in eastern India. We followed the ensemble of spatial prediction models using species presence data and five environmental variables. We used least-cost path and circuit theory approaches to identify the spatial connectivity between core habitats for Asian elephants. The results revealed that normalized difference vegetation index (NDVI; variable importance 42%) and terrain ruggedness (19%) are the most influential variables for predicting habitat suitability of species within the study area. Our habitat suitability map estimated 14.6% of Odisha's geographical area (c. 22,442 km2) as highly suitable and 13.3% (c. 20,464 km2) as moderate highly suitable. We identified 58 potential linkages to maintain the habitat connectivity across study area. Furthermore, we identified pinch points, bottlenecks, and high centrality links between core habitats. Our study offers management implications for long-term landscape conservation for Asian elephants in Odisha and highlights priority zones that can help maintain spatial links between elephant habitats.

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.

Spatio-temporal variations of climate along possible African-Arabian routes of H. sapiens expansion

Eastern Africa and Arabia were major hominin hotspots and critical crossroads for migrating towards Asia during the late Pleistocene. To decipher the role of spatiotemporal environmental change on human occupation and migration patterns, we remeasured the marine core from Meteor Site KL 15 in the Gulf of Aden and reanalyzed its data together with the aridity index from ICDP Site Chew Bahir in eastern Africa and the wet-dry index from ODP Site 967 in the eastern Mediterranean Sea using linear and nonlinear time series analysis. These analyses show major changes in the spatiotemporal paleoclimate dynamics at 400 and 150 ka BP (thousand years before 1950), presumably driven by changes in the amplitude of the orbital eccentricity. From 400 to 150 ka BP, eastern Africa and Arabia show synchronized wet-dry shifts, which changed drastically at 150 ka BP. After 150 ka BP, an overall trend to dry climate states is observable, and the hydroclimate dynamics between eastern Africa and Arabia are negatively correlated. Those spatio-temporal variations and interrelationships of climate potentially influenced the availability of spatial links for human expansion along those vertices. We observe positively correlated network links during the supposed out-of-Africa migration phases of H. sapiens. Furthermore, our data do not suggest hominin occupation phases during specific time intervals of humid or stable climates but provide evidence of the so far underestimated potential role of climate predictability as an important factor of hominin ecological competitiveness.

Critical behavior of cascading failures in overloaded networks.

While network abrupt breakdowns due to overloads and cascading failures have been studied extensively, the critical exponents and the universality class of such phase transitions have not been discussed. Here, we study breakdowns triggered by failures of links and overloads in networks with a spatial characteristic link length ζ. Our results indicate that this abrupt transition has features and critical exponents similar to those of interdependent networks, suggesting that both systems are in the same universality class. For weakly embedded systems (i.e., ζ of the order of the system size L) we observe a mixed-order transition, where the order parameter collapses following a long critical plateau. On the other hand, strongly embedded systems (i.e., ζ≪L) exhibit a pure first-order transition, involving nucleation and the growth of damage. The system's critical behavior in both limits is similar to that observed in interdependent networks.

Seismological Constraints on the Causes of Intraplate Earthquakes in Yangjiang Region, South China

AbstractLarge earthquakes that occur away from plate boundaries (i.e., large intraplate earthquakes), though rare, could cause heavy damage. Understanding their causes could help with seismic hazard assessment. In this study, we image the seismogenic structure under the 1969 Ms6.4 Yangjiang intraplate earthquake within the stable South China Block with a high‐resolution three‐dimensional shear‐wave velocity model constructed from ambient noise tomography based on a dense nodal seismic array. The model images relatively low velocities at a fault intersection region from the surface to at least 13 km depth in the Yangjiang seismogenic zone. Given spatial links among the intersection zone of faults, surface seawater, and low velocities, we interpret the low velocities to reflect the presence of seawater‐filled, highly fractured rocks created by the intersection of faults. We infer that the seawater infiltration through densely‐fractures could have elevated pore pressure in the long term, as evidenced by low earthquake b values at 8–13 km depths. We hypothesize that this long‐term seawater infiltration could have lowered the stress threshold of earthquake occurrence and contributed to the generation of the 1969 Ms6.4 Yangjiang earthquake. We propose that the large intraplate earthquakes within stable plates tend to occur in regional weakening regions (e.g., fault intersections) favorable to stress buildup. Given sufficient tectonic stress accumulation, the long‐term hydrologically driven crustal stress variations could play an essential role in triggering large intraplate earthquakes. This study could potentially contribute to improving seismic hazard assessment in the Guangdong‐Hong Kong‐Macao‐Greater‐Bay‐Area, China.

Dissolved Oxygen Forecasting for Lake Erie’s Central Basin Using Hybrid Long Short-Term Memory and Gated Recurrent Unit Networks

Dissolved oxygen (DO) concentration is a pivotal determinant of water quality in freshwater lake ecosystems. However, rapid population growth and discharge of polluted wastewater, urban stormwater runoff, and agricultural non-point source pollution runoff have triggered a significant decline in DO levels in Lake Erie and other freshwater lakes located in populated temperate regions of the globe. Over eleven million people rely on Lake Erie, which has been adversely impacted by anthropogenic stressors resulting in deficient DO concentrations near the bottom of Lake Erie’s Central Basin for extended periods. In the past, hybrid long short-term memory (LSTM) models have been successfully used for the time-series forecasting of water quality in rivers and ponds. However, the prediction errors tend to grow significantly with the forecasting period. Therefore, this research aimed to improve the accuracy of DO forecasting models by taking advantage of Lake Erie’s real-time water quality (water temperature and DO concentration) monitoring network to establish temporal and spatial links between adjacent monitoring stations. We developed hybrid LSTM models that combine LSTM, convolutional neuron network LSTM (CNN-LSTM), hybrid CNN with gated recurrent unit (CNN-GRU) models, and convolutional LSTM (ConvLSTM) to forecast near-bottom DO concentrations in Lake Erie’s Central Basin. These hybrid LSTM models improve their capacity to handle complicated datasets with spatial and temporal variability. These models can serve as accurate and reliable tools for forecasting DO concentrations in freshwater lakes to help environmental protection agencies better access and manage the health of these vital ecosystems. Following analysis of a 21-site Lake Erie dataset for 2020 and 2021, the ConvLSTM model emerged as the most accurate and reliable, boasting an MSE of 0.51 mg/L, MAE of 0.42 mg/L, and an R-squared of 0.95 over the 12 h prediction range. The model foresees future hypoxia in Lake Erie. Notably, the temperature near site 713 holds significance for Central Basin DO forecasting in Lake Erie, as indicated by outcomes derived from the Shapley additive explanations (SHAP).

The Sacred Building and the City: Decoding the Formal Interface between Public Space and Community

The reflection on sacred places continues to assume significant relevance today in urban space production. The public value of sacred buildings has consolidated over time an aggregating sense of community, representing spaces for meeting and sharing. Their historical relevance as spaces for meditation represents for mankind places of personal reflection, while they have always played an important role in the city and in its symbolic and spatial structure. Thus, starting from the hypothesis that the sacred space is affirmed as an interface, because it welcomes the individual and serves the community, we examine the architectural features that enhance this ambivalence, exposing transition systems between private and collective spaces, seeking to systematize essential composition matrices for new urban spaces for public use. Assuming Lisbon as a framework, this article proposes a comparative reading between two paradigmatic buildings—Sagrado Coração de Jesus Church and the New Mosque of Lisbon—with similar goals according to the relationship between architecture, place sacrality, and the urban public space. Methodologically, drawing is used as an interpretative tool and, through formal decomposition, this article tries to demonstrate that these buildings are the result of a reflection deeply determined by the value of the place’s identity in the city’s public space system. According to these case studies, sacred buildings are conceived based on formal and spatial links that are rooted in Lisbon’s urban layout. It is sacred buildings that are at the origin of urban places for public use. Each one of these buildings share an idea of architecture with an urban and public role which integrates the objects with the shape of the city and contradicts the tendency for the dissociation between urban elements. In a way, they can be considered paradigmatic examples of architecture with an urban vocation.

Deep attention transformer nets for accurate analysis of oil spilled images to minimize pollution in the marine environment

Oil spills in maritime areas pose a serious environmental risk, wreaking havoc on marine ecosystems, coastal habitats, and local residents. An accurate and timely evaluation of oil spill occurrences and extent is critical for effective pollution control and mitigation. In this study, we present a novel and cutting-edge approach for analyzing oil-spilled images using Deep Attention Transformer Nets (DATN) with Collective Intelligence (CI), with the goal of reducing pollution in the marine environment. This method takes advantage of deep learning capability, notably the incorporation of transformer-based attention processes, to improve the identification and measurement of oil spills in satellite and aerial images. The DATN model is intended to learn complicated features from images automatically, capturing complex patterns associated with oil spills and their surrounding context. The model chooses focus on key regions and add spatial links by using attention mechanisms, allowing for a more comprehensive understanding of the environmental influence. We thoroughly test DATN performance using a variety of datasets encompassing various oil spill scenarios and environmental circumstances. The results show that DATN surpasses standard approaches and other deep learning models in recognizing oil spill regions, with excellent accuracy, precision, and recall rates. Furthermore, the model has strong generalization capabilities across a wide range of image sources and situations.

Putting space into trait ecology: Trait, environment and biodiversity relationships at multiple spatial scales

Abstract Ecological processes such as environmental filtering and biotic interactions that shape species' traits and community diversity often vary with geographic distance, potentially generating spatial structures in trait variation, covariation and biodiversity data. Understanding spatial structures of trait, environment and biodiversity, or the spatial link between those factors, is fundamental to identifying spatially explicit assembly processes or biodiversity distributions in spatially heterogeneous landscapes but remains unclear. To address the issue, we gathered individual‐level leaf and diameter traits data paired with environmental data from a 4.8 ha subtropical Chinese forest and divided the forest into 25, 100, 400 and 1936 m2 grids representing contrasting spatial grains. Using Moran's correlograms, we quantified the spatial structures of trait variation and covariation, environmental conditions and biodiversity. We assessed the links between those variables using path analyses. Most variables were spatially positively autocorrelated. However, trait mean was more autocorrelated than trait variation or covariation, and intraspecific trait variation was more autocorrelated than interspecific variation. Autocorrelations in those community properties were generally weak at the large grain. Path analyses indicated positive associations between interspecific trait variation and species diversity at a very small to medium scale and a positive association between intraspecific variation and small‐scale functional diversity. Trait covariation constrained biodiversity, and multi‐trait means were negatively linked to very small‐ to medium‐scale species diversity but positively to medium‐ and large‐scale functional diversity. Patterns regarding multi‐trait community structure–environment–biodiversity associations were generally held for individual traits. However, depending on the trait, spatial scale and plant ontogenic stage, the pattern's strength changed, or occasionally, their sign reversed. We attribute spatial patterns in multi‐trait mean and covariation to scale‐dependent variation in environmental heterogeneity and trait variation to scale‐dependent competition. Synthesis. Our study provides novel insights into spatial and scale‐dependent variability in functional community structure, environment and biodiversity, and their relationships. Our results demonstrate the usefulness of spatial trait analyses in identifying scale‐dependent assembly processes or finding the importance of processes to biodiversity distributions in spatially heterogeneous landscapes. A spatially explicit perspective is thus helpful for the progress of trait ecology.