Spatial Aggregation Research Articles

Ensuring spatial scalability with temporal-wise spatial attentive pooling for temporal action detection

Recent temporal action detection models have focused on end-to-end trainable approaches to utilize the representational power of backbone networks. Despite the advantages of end-to-end trainable methods, these models still employ a small spatial resolution (e.g., 96 × 96) due to the inefficient trade-off between computational cost and spatial resolution. In this study, we argue that a simple pooling method (e.g., adaptive average pooling) acts as a bottleneck at the spatial aggregation part, restricting representational power. To address this issue, we propose a temporal-wise spatial attentive pooling (TSAP), which alleviates the bottleneck between the backbone and the detection head using a temporal-wise attention mechanism. Our approach mitigates the inefficient trade-off between spatial resolution and computational cost, thereby enhancing spatial scalability in temporal action detection. Moreover, TSAP is adaptable to previous end-to-end approaches by simply replacing the spatial pooling part. Our experiments demonstrated the essential role of spatial aggregation, and consistent improvements are observed by incorporating TSAP into previous end-to-end methods.

Comprehensively Assessing PM2.5 Exposure Across Mainland China with Estimated Concentrations Considering Spatial Aggregation

Comprehensively Assessing PM2.5 Exposure Across Mainland China with Estimated Concentrations Considering Spatial Aggregation

Scale effects-aware bottom-up population estimation using weakly supervised learning

ABSTRACT Fine-scale population estimation (FPE) is crucial for urban management. After training, the bottom-up FPE models can be applied independently of census data. However, given the lack of real fine-scale population data, the existing bottom-up methods typically apply models trained on coarse-grained census data to FPE directly, causing estimation bias induced by scale effects. Traffic analysis zones (TAZs) balance geo-semantics and fine granularity, but their potential as population analysis units has not been fully exploited. Hence, we developed a weakly-supervised TAZ-scale bottom-up population estimation method (WSTP). Specifically, to mitigate scale effects, the weakly-supervised training procedure involves fine-scale feature input, model prediction, spatial aggregation, and coarse-scale supervision is designed to ensure that WSTP consistently focuses on FPE throughout the training and prediction phases. To enable weakly-supervised training using census data, we treated TAZs as graph nodes and designed a Spatial Aggregation Layer to aggregate TAZ-scale population predictions into communities. Given the diverse distribution patterns across age groups, we decomposed the FPE task by age groups. The experiments showed that WSTP significantly outperformed the baselines with R2 values of 0.821 and 0.785 at the community and TAZ scales, respectively, indicating that WSTP can mitigate scale effects and produce high-resolution, accurate population data.

Understanding the impact of modifiable areal unit problem on urban vitality and its built environment factors

ABSTRACT Urban vitality is an essential reference for formulating urban planning and urban development policies, while the Modifiable Areal Unit Problem (MAUP) is a crucial issue that affects the accurate assessment of urban vitality and the understanding of its driving factors. Currently, MAUP in urban vitality studies has not received sufficient attention, and there are limited investigations on whether MAUP significantly affects the spatial autocorrelation of urban vitality. Moreover, existing studies have mainly focused on the impact of individual factors on urban vitality, with minor consideration of the interactions between factors and their effects on urban vitality at different spatial scales. This study takes the Pearl River Delta Core (PRDC) region as the research area. It used the Optimal Parameters-based Geographic Detector (OPGD) model and spatial autocorrelation to analyze the impact of the MAUP on urban vitality and its Built Environment (BE) factors. The results indicated that (1) the five types of BE driving factors, including work, living, rest, traffic, and Points of Interest (POI) diversity, were spatially sensitive and exhibited scale threshold effects. (2) The zoning schemes demonstrated the ability to alter the explanatory power (q-value) of driving factors and their trends. The spatial granularity and aggregation level of the grid-based zoning scheme, NPP-VIIRS nighttime light zoning scheme, and land surface temperature zoning scheme exhibited a logarithmic relationship, where spatial autocorrelation levels decreased with increasing aggregation level. (3) Compared to single-factor effects, the interactive effects of BE factors exhibited a higher q-value for urban vitality. Additionally, the q-values for factor interactions were more stable in multiple spatial scales than those q-values for individual factors. This study provides decision-making references for urban planners and policymakers to accurately assess urban vitality and find balanced, improved paths to enhance it.

S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images

Accurate liver tumor segmentation is crucial for aiding radiologists in hepatocellular carcinoma evaluation and surgical planning. While convolutional neural networks (CNNs) have been successful in medical image segmentation, they face challenges in capturing long-term dependencies among pixels. On the other hand, Transformer-based models demand a high number of parameters and involve significant computational costs. To address these issues, we propose the Spatial and Spectral-learning Double-branched Aggregation Network (S2DA-Net) for liver tumor segmentation. S2DA-Net consists of a double-branched encoder and a decoder with a Group Multi-Head Cross-Attention Aggregation (GMCA) module, Two branches in the encoder consist of a Fourier Spectral-learning Multi-scale Fusion (FSMF) branch and a Multi-axis Aggregation Hadamard Attention (MAHA) branch. The FSMF branch employs a Fourier-based network to learn amplitude and phase information, capturing richer features and detailed information without introducing an excessive number of parameters. The FSMF branch utilizes a Fourier-based network to capture amplitude and phase information, enriching features without introducing excessive parameters. The MAHA branch incorporates spatial information, enhancing discriminative features while minimizing computational costs. In the decoding path, a GMCA module extracts local information and establishes long-term dependencies, improving localization capabilities by amalgamating features from diverse branches. Experimental results on the public LiTS2017 liver tumor datasets show that the proposed segmentation model achieves significant improvements compared to the state-of-the-art methods, obtaining dice per case (DPC) 69.4 % and global dice (DG) 80.0 % for liver tumor segmentation on the LiTS2017 dataset. Meanwhile, the pre-trained model based on the LiTS2017 datasets obtain, DPC 73.4 % and an DG 82.2 % on the 3DIRCADb dataset.

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.

Research on Efficient Feature Generation and Spatial Aggregation for Remote Sensing Semantic Segmentation

Semantic segmentation algorithms leveraging deep convolutional neural networks often encounter challenges due to their extensive parameters, high computational complexity, and slow execution. To address these issues, we introduce a semantic segmentation network model emphasizing the rapid generation of redundant features and multi-level spatial aggregation. This model applies cost-efficient linear transformations instead of standard convolution operations during feature map generation, effectively managing memory usage and reducing computational complexity. To enhance the feature maps’ representation ability post-linear transformation, a specifically designed dual-attention mechanism is implemented, enhancing the model’s capacity for semantic understanding of both local and global image information. Moreover, the model integrates sparse self-attention with multi-scale contextual strategies, effectively combining features across different scales and spatial extents. This approach optimizes computational efficiency and retains crucial information, enabling precise and quick image segmentation. To assess the model’s segmentation performance, we conducted experiments in Changge City, Henan Province, using datasets such as LoveDA, PASCAL VOC, LandCoverNet, and DroneDeploy. These experiments demonstrated the model’s outstanding performance on public remote sensing datasets, significantly reducing the parameter count and computational complexity while maintaining high accuracy in segmentation tasks. This advancement offers substantial technical benefits for applications in agriculture and forestry, including land cover classification and crop health monitoring, thereby underscoring the model’s potential to support these critical sectors effectively.

Study on general industrial solid waste and carbon reduction in China: Coupling coordination model, life cycle assessment and environmental safety control

The production of general industrial solid waste (GISW) which has grown rapidly along with the economy, has become a bottleneck that is impeding the sustainable and healthy growth of society and the economy. At the same time, China, as the country with the largest carbon emissions at present, has set a double carbon target, so China is faced with the dual goal of promoting the synergy of GISW pollution and carbon reduction. This paper systematically reviews the actual production data, literature reports, standard specifications, etc. of GISW and carbon dioxide emission in China, and processes the data with synergistic effect model. Both GISW and carbon emission in China show an obvious spatial aggregation effect, and the coupling and coordination index of both grows from moderate maladjustment to senior coordination stage, and the synergistic effect of different regions gradually increases. Selecting typical pollutants from key provinces analyzed based on coupling coordination degree, and the life cycle assessment was performed using SimaPro 9.0 software, the results show that fly ash is more destructive to the environment and causes higher carbon emissions than the other two GISW, with carbon emission equivalent being about 1.8 times that of tailings and 1.6 times that of EMR. Compared with the carbon emission equivalent of 1.12 × 103 kg CO2 eq in the preparation of traditional cementitious materials, the preparation of coal-based cementitious materials generates less greenhouse gases with carbon emission equivalent of about 1.06 × 103 kg CO2 eq, which has a significant solid waste consumption effect as well as carbon reduction effect. Meanwhile, from the GISW storage process, a disposal environmental safety control system is established mainly for heavy metals, volatile organic compounds and atmospheric pollutants by comprehensively referring to literature and standard specifications.

IVehicles: Spatial Feature Aggregation Network for Lane Detection Assistance System

iVehicles: Spatial Feature Aggregation Network for Lane Detection Assistance System

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.

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

Soil moisture (SM) is a crucial environmental variable, and it plays an important role in energy and water cycles. SM data retrieval based on microwave satellite remote sensing has garnered significant attention due to its spatial continuity, wide observational coverage, and relatively low cost. Validating the accuracy of satellite remote sensing SM products is a critical step in enhancing data credibility, which plays a vital role in ensuring the effective application of satellite remote sensing data across various fields. Firstly, this study focused on Henan Province and evaluated the accuracy of the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture (SPL3SMP_E) product along with its application in agriculture. The evaluation was based on in situ SM data from 55 stations in Henan Province. The assessment metrics used in this study include mean difference (MD), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the Pearson correlation coefficient (R). The time span of this study is from 2017 to 2020. The evaluation results indicated that the SPL3SMP_E soil moisture product performs well, as reflected by an ubRMSE value of 0.045 (m3/m3), which was relatively close to the product’s design accuracy of 0.04 (m3/m3). Moreover, the accuracy of the product was unaffected by temporal factors, but the product exhibited strong spatial aggregation, which was closely related to land use types. Then, this study explored the response of the SPL3SMP_E product to irrigation signals. The precipitation and irrigation data from Henan Province were employed to investigate the response of the SPL3SMP_E soil moisture product to irrigation. Our findings revealed that the SPL3SMP_E soil moisture product was capable of capturing over 70% of irrigation events in the study area, indicating its high sensitivity to irrigation signals in this region. In this study, the SPL3SMP_E product was also employed for monitoring agricultural drought in Henan Province. The findings revealed that the collaborative use of the SPL3SMP_E soil moisture product and machine learning algorithms proves highly effective in monitoring significant drought events. Furthermore, the integration of multiple indices demonstrated a notable enhancement in the accuracy of drought monitoring. Such an evaluation holds significant implications for the effective application of satellite remote sensing SM data in agriculture and other domains.

Investigation on cutting deformation behavior of Al/SiCp composites considering particle feature distributions

Al/SiCp composite machining is considerably difficult owing to the presence of abrasive particles that results in weak synergetic deformation capacity during cutting. Therefore, to enhance the machinability of Al/SiCp composites, investigations on the cutting deformation behavior are imperative through finite element simulation. Aiming to address the limitations of conventional finite element models in accurately representing the actual microstructure of Al/SiCp composites, this study proposes a realistic microstructure-based finite element model to investigate the cutting deformation and damage behavior. The microstructural characteristics of the composites namely, particle feature distributions, including size distribution, aspect ratio distribution, and spatial aggregation of SiC particles, are analyzed and extracted based on the microstructure image of composite and the Gaussian stochastic model. Then, the particle feature distributions are incorporated into the microstructure-based model for the first time. Besides, all the failure mechanisms (i.e., the brittle fracture of SiC particles, the elastoplastic damage of Al matrix, as well as the particle-matrix decohesion) and the tool-chip contact behavior are comprehensively integrated into the modeling. The experimental results show that as the particle volume fraction and size increase, the chip radius and the number of chip curls decrease, the chips vary from little waviness to prominent saw-tooth type, and more particles are broken or debonded. According to the comparison with experimental results, the finite element model can effectively capture the deformation and failure process, as well as the characteristics of chip morphology, cutting force, and machined surface quality. In addition, the comparisons further demonstrate that SiC particles with large size, slender shape, and aggregation are prone to fracture and detachment in the cutting deformation. Finally, the influences of the particle feature distributions on the machinability of Al/SiCp composites are discussed in detail. It is found that decreasing the dispersion degree of particle size and aspect ratio distribution as well as minimizing the degree of particle clustering degree benefit the machinability of Al/SiCp composites.

An interpretable operating condition partitioning approach based on global spatial structure compensation-local temporal information aggregation self-organizing map for complex industrial processes

Effective and interpretable partitioning of operating conditions in complex industrial processes is crucial for ensuring the efficiency and reliability of emerging integrated production processes. However, contemporary operation–condition partitioning models fall short in this domain for two main reasons. First, the parameters related to purely data-driven methods fail to involve physically meaningful semantics, resulting in suboptimal accuracy and interpretability. Second, there exists a spatial–temporal coupling interaction among process variables that reflects physically meaningful spatial–temporal correlations, further impeding effective interpretation. To address these challenges, an interpretable operation–condition partitioning method based on a Global Spatial Structure Compensation-Local Temporal Information Aggregation Self-Organizing Map is proposed. This approach formulates the operation–condition partitioning problem as a spatio-temporal feature extraction based on the operation–condition partitioning problem. First, a local separable aggregation (LSA) model based on the informer backbone is introduced to partition industrial variables into interpretable time-local groups. This ensures that the model can learn the underlying factors of the condition’s occurrence, thereby providing a reliable basis for interpretable root cause analysis. Next, a Global Spatial Feature Compensator (GSFC) is introduced that leverages the process mechanism knowledge and graph network to establish explicit spatial interrelationships among time-local groups in the LSA algorithm, thereby enhancing the model's inferential capabilities and security assurance. Subsequently, a spatiotemporal self-organizing map is designed to integrate the aforementioned spatial and temporal features, facilitating precise operation–condition partitioning decisions. Finally, the effectiveness and understandability of the proposed method are validated through comprehensive experiments in a real industrial application case study.

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.

Abstract 876: spatialGE: Empowering researchers to study the tumor microenvironment leveraging spatial transcriptomics

Abstract The use of single-cell RNA-seq (scRNA-seq) to understand the complexity of the tumor microenvironment (TME) has been pivotal in advancing cancer research and treatment strategies. Nevertheless, locating the individual TME components in their spatial contexts is essential to understanding the complexity of the TME, tumorigenesis, metastasis, and drug response. The recent popularity of spatially resolved transcriptomics (SRT) allows researchers to describe the TME and their spatial neighborhoods; however, SRT data analysis can be challenging for non-data scientists. Thus, we have developed spatialGE, a point-and-click web application designed to empower researchers in the analysis of SRT data. Our application offers a suite of functionalities, including the visualization of spatial gene expression activity maps, quantification of the spatial heterogeneity, testing of pathway-level spatial aggregation, tissue domain/niche detection, detection of spatial gradients in expression patterns, and differential expression analysis. Furthermore, spatialGE allows direct comparative analysis of gene expression with the morphology in tissue images, as well as methods for cell/domain phenotyping. Data input has been streamlined and made flexible to receive outputs from multiple SRT platforms. Analyses and results are stored within user-defined projects to facilitate accessibility at any time. We demonstrate the usefulness of spatialGE through its application to leptomeningeal metastatic melanoma (LMM) tissues assayed with the Visium platform. The gene expression tissue domain detection and gradient analysis in spatialGE indicated that SERPINA3 expression is higher in tumor regions closer to stroma. This finding led to cell culture experiments indicating that knocking down SERPINA3 results in the sensitization of tumors to MAPK-targeting therapy. These results highlight the utility of spatialGE for exploring SRT data and the empowerment of cancer researchers to explore the spatial architecture of the TME. Citation Format: Oscar Ospina, Roberto Manjarres-Betancur, Guillermo Gonzalez-Calderon, Alex C. Soupir, Inna Smalley, Kenneth Tsai, Joseph Markowitz, Anders Berglund, Xiaoqing Yu, Brooke L. Fridley. spatialGE: Empowering researchers to study the tumor microenvironment leveraging spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 876.

Spatial structuring of Mediterranean fisheries landings in relation to their seasonal and long-term fluctuations

The Western Mediterranean fisheries significantly contribute to the regional blue economy, despite evidence of ongoing, widespread overexploitation of stocks. Understanding the spatial distribution and population dynamics of species is crucial for comprehending fisheries dynamics combining local and regional scales, although the underlying processes are often neglected. In this study, we aimed to (i) evaluate the seasonal and long-term spatio-temporal fluctuations of crustacean, cephalopod, and fish populations in the Western Mediterranean, (ii) determine whether these fluctuations are driven by the spatial structure of the fisheries or synchronic species fluctuations, and (iii) compare groupings according to the individual species and life history-based groups. We used dynamic factor analysis to detect underlying patterns in a Landing Per Unit Effort (LPUE) time series (2009–2020) for 23 commercially important species and 33 ports in the Western Mediterranean. To verify the spatial structure of ports and species groupings we investigated the seasonal and long-term spatio-temporal fluctuations and common LPUE trends that exhibit non-homogeneous and species-specific trends, highlighting the importance of life history, environmental and demographic preferences. Long-term trends revealed spatial segregation with a north-south gradient, demonstrating complex population structures of Western Mediterranean resources. Seasonal patterns exhibited a varying spatial aggregation based on species-port combinations. These findings can inform the Common Fishery Policy on gaps challenging their regionalisation objectives in the Mediterranean Sea. We highlight the need for a nuanced and flexible approach and a better understanding of sub-regional processes for effective management and conservation – a current challenge for global fisheries. Our LPUE approach provides insight into population dynamics and changes in regional fisheries, relevant beyond the Mediterranean Sea.

Spatial pattern of seed arrival has a greater effect on plant diversity than does soil heterogeneity in a grassland ecosystem

Background and AimsSpecies diversity is expected to increase with environmental heterogeneity. For plant communities, this pattern has been confirmed by numerous observational studies. Yet, experimental studies yield inconsistent results potentially because of how experiments create soil heterogeneity or because seeds were sown homogeneously. Using a field experiment, we tested how soil heterogeneity, plant spatial aggregation via seed arrival, and grain size influence plant species richness in a restored grassland.MethodsWe manipulated soil heterogeneity and seed arrival in 0.2 × 0.2 or 0.4 × 04 m patches within each 4.0 × 4.6 m plot and allowed community assembly to occur for 4 growing seasons.ResultsDespite quantifiable soil differences, soil heterogeneity did not impact total or sown species richness, but did weakly influence non-sown richness. Richness differences were driven by non-sown plant species that likely exhibited higher establishment in aggregated plots due to decreased interspecific competition and conspecific facilitation.ConclusionOur results suggest that fine-scale soil heterogeneity weakly affects prairie plant diversity, but heterogeneous plant spatial structure can have a stronger effect on diversity. These results suggest that plant colonization may be the primary source of environmental heterogeneity and may explain inconsistent results from soil heterogeneity experiments.

Evolving demographics: a dynamic clustering approach to analyze residential segregation in Berlin

This paper examines the phenomenon of residential segregation in Berlin over time using a dynamic clustering analysis approach. Previous research has examined the phenomenon of residential segregation in Berlin at a high spatial and temporal aggregation and statically, i.e. not over time. We propose a methodology to investigate the existence of clusters of residential areas according to migration background, age group, gender, and socio-economic dimension over time. To this end, we have developed a sequential mixed methods approach that includes a multivariate kernel density estimation technique to estimate the density of subpopulations and a dynamic cluster analysis to discover spatial patterns of residential segregation over time (2009-2020). The dynamic analysis shows the emergence of clusters on the dimensions of migration background, age group, gender and socio-economic variables. We also identified a structural change in 2015, resulting in a new cluster in Berlin that reflects the changing distribution of subpopulations with a particular migratory background. Finally, we discuss the findings of this study with previous research and suggest possibilities for policy applications and future research using a dynamic clustering approach for analyzing changes in residential segregation at the city level.

A new perspective on the spatial, environmental, and metacommunity controls of local biodiversity

Influential ecological research in the 1980s, elucidating that local biodiversity (LB) is a function of local ecological factors and the size of the regional species pool (γ-diversity), has prompted numerous investigations on the local and regional origins of LB. These investigations, however, have been mostly limited to single scales and target groups and centered exclusively on γ-diversity. Here we developed a unified framework including scale, environmental factors (heterogeneity and ambient levels), and metacommunity properties (intraspecific spatial aggregation, regional evenness, and γ-diversity) as hierarchical predictors of LB. We tested this framework with variance partitioning and structural equation modeling using subcontinental data on stream diatoms, insects, and fish as well as local physicochemistry, climate, and land use. Pure aggregation + regional evenness outperformed pure γ-diversity in explaining LB across groups. The covariance of the environment with aggregation + regional evenness rather than with γ-diversity generally explained a much greater proportion of the variance in diatom and insect LB, especially at smaller scales. Thus, disregarding aggregation and regional evenness, as commonly done, may lead to gross underestimation of the pure metacommunity effects and the indirect environmental effects on LB. We examined the shape of the local-regional species richness relationship, which has been widely used to infer local vs. regional effects on LB. We showed that this shape has an ecological basis, but its interpretation is not straightforward. Therefore, we advocate that the variance partitioning analysis under the proposed framework is adopted instead. In diatoms, metacommunity properties had the greatest total effects on LB, while in insects and fish, it was the environment, suggesting that larger organisms are more strongly controlled by the environment. Broader use of our framework may lead to novel biogeographical insights into the drivers of LB and improved projections of its trends along current and future environmental gradients.

Camera trapping and telemetry for detecting and quantifying animal interactions: Not anything goes

Telemetry using Global Positioning Systems (GPS) and camera trapping are two widely used approaches to detect animal interactions involving wildlife. However, while the former follows an individual-based approach, the latter follows a population-based approach, which may lead to different outcomes when describing interaction networks. In this study, we compared the performance of GPS telemetry, regular grid camera trapping (CT-RG), and resource monitoring camera trapping (in which cameras are placed monitoring a valuable resource for animals, such as water or food; CT-RM) in detecting and quantifying animal interactions. We used spatially explicit simulated trajectories of 150 individuals of 3 imaginary species. We simulated 9 different scenarios of resource visiting frequency and spatial resource aggregation in the environment to assess the potential influence of animal behavior and landscape structure. The simulated trajectories were sampled using the three approaches with six different sampling efforts each, and the direct and indirect interactions between the three species were quantified. Additionally, CT-RG and CT-RM were applied in Doñana National Park to evaluate their differences and limitations in field application. In terms of simulation, CT-RM offered the best performance in detecting direct interactions, and GPS achieved good performance when using a high number of fixes per hour. Both CT-RM and GPS also achieved good performance in detecting indirect interactions. CT-RG, on the other hand, missed most direct and indirect interactions, but correctly identified the pairs of species that interacted indirectly at some point. In the field trial, CT-RM and CT-RG detected almost the same number of species pairs interacting indirectly. However, CT-RG missed at least half of the species pairs that interacted directly. The spatial aggregation of the resource favors the performance of GPS over CT-RM, while the opposite occurs when the frequency of animal visits to the resource increases. To summarize, CT-RM is the most reliable method for detecting interactions when resource monitoring is feasible (e.g., small ponds in arid areas). Otherwise, GPS may show better performance, although it may miss direct interactions if a high fix rate is not used. Finally, the use of CT-RG should be limited to identify which species could potentially interact indirectly, but not to quantify these interactions.