Spatial Distance Research Articles

Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models

Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model’s performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.

Assessing repeatability of spatial trajectories

Abstract Repeatability involves the assessment of the agreement among repeated measurements from the same cluster of subjects, and this concept has been widely used in different scientific fields when data is structured in clusters. In the context of spatial trajectories, a degree of repeatability implies that individual trips can be distinguished from those of other individuals. Repeatability is usually assessed by the intraclass correlation coefficient (ICC), which is defined as the proportion of the total variance accounted for by among‐subject variability. However, where data are spatial trajectories the common approach to estimate the ICC does not apply because data involves sets of ordered spatial locations rather than single values. In this work, a novel approach based on spatial distances is proposed to estimate the ICC to assess the repeatability of spatial trajectories. The methodology is illustrated with a real case example of the flight trajectories of 36 Audouin's gulls (Ichthyaetus audouinii) moving through a heterogeneous landscape over a period of 18 days. Additionally, simulations were used to evaluate the performance of the approach under various scenarios. I demonstrate that ICC can be estimated on complex, spatially ordered data such as spatial trajectories, when using the appropriate spatial distance.

Hippocampal contributions to novel spatial learning are both age-related and age-invariant.

Older adults show declines in spatial memory, although the extent of these alterations is not uniform across the healthy older population. Here, we investigate the stability of neural representations for the same and different spatial environments in a sample of younger and older adults using high-resolution functional MRI of the medial temporal lobes. Older adults showed, on average, lower neural pattern similarity for retrieving the same environment and more variable neural patterns compared to young adults. We also found a positive association between spatial distance discrimination and the distinctiveness of neural patterns between environments. Our analyses suggested that one source for this association was the extent of informational connectivity to CA1 from other subfields, which was dependent on age, while another source was the fidelity of signals within CA1 itself, which was independent of age. Together, our findings suggest both age-dependent and independent neural contributions to spatial memory performance.

Ionospheric compensation in L-band InSAR time-series: Performance evaluation for slow deformation contexts in equatorial regions

Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) is the only geodetic technique allowing to measure ground deformation down to mm/yr over continuous areas. Vegetation cover in equatorial regions favors the use of L-band SAR data to improve interferometric coherence. However, the electron content of ionosphere, affecting the propagation of the SAR signal, shows particularly strong spatio-temporal variations near the equator, while the dispersive nature of the ionosphere makes its effect stronger on low-frequencies, such as L-band signals. To tackle this problem, range split-spectrum method can be implemented to compensate the ionospheric phase contribution. Here, we apply this technique for time-series of ALOS-PALSAR data, and propose optimizations for low-coherence areas. To evaluate the efficiency of this method to retrieve subtle deformation rates in equatorial regions, we compute time-series using four ALOS-PALSAR datasets in contexts of low to medium coherence, showing slow deformation rates (mm/yr to cm/yr). The processed tracks are located in Ecuador, Trinidad and Sumatra, and feature 15 to 19 acquisitions including very high, dominating ionospheric noise, corresponding to equivalent displacements of up to 2 m. The correction method performs well and allows to reduce drastically the noise level due to ionosphere, with significant improvement compared with a simple plane fitting method. This is due to frequent highly non-linear patterns of perturbation, characterizing equatorial TEC distribution. We use semivariograms to quantify the uncertainty of the corrected time-series, highlighting its dependence on spatial distance. Thus, using ALOS-PALSAR-like archive, one can expect a detection threshold on the Line-of-Sight velocity ranging between 3 and 6 mm/yr, depending on the spatial wavelength of the signal to be observed. These values are consistent with the accuracy derived from the comparison of velocities between two tracks in their overlapping area. In the case studies that we processed, the time-series corrected from ionosphere allows to retrieve accurately fault creep and volcanic signal but it is still too noisy for retrieving tiny long-wavelength signals such as slow (mm/yr) interseismic strain accumulation.

Retrieving hourly seamless PM2.5 concentration across China with physically informed spatiotemporal connection

Seamless hourly surface fine particulate matters (PM2.5) datasets are urgently needed for environmental and epidemiologic studies. Most existing models rely on satellite AOD with spatial gaps and the time-geolocation information not directly physically related to ground-level PM2.5 as input, leading to several problems, e.g., for daytime only, spatial discontinuity. By exploring spatiotemporal connection, here we propose a novel framework for improving reanalysis AOD with satellite AOD and retrieving seamless PM2.5 concentration. With the wavelet decomposition on the meteorological / AOD time-series and the machine learning algorithm ‘CatBoost’, the grid cells are well connected based on the meteorological/AOD similarity (quasi-Lagrangian description), rather than the spatial distance (Eulerian description). Our new AOD dataset (compared with AERONET AOD, R2 = 0.60, RMSE = 0.19) combines the advantages of reanalysis AOD (all-day hourly; seamless) and satellite AOD (high accuracy and spatial resolution). The new PM2.5 estimation strategy performed excellent in cross-validation (spatial CV R2/RMSE = 0.84/14.57 μg/m3) and mapping (no spatial discontinuity). With the help of new AOD dataset, the PM2.5 estimation exhibits significant improvement (R2: increased from 0.60 to 0.65, RMSE: reduced from 21.52 to 20.10 μg/m3) on retrieving PM2.5 at ground monitoring site sparse area (the spatial distance to the closest site is over 20 km). Such high temporal-resolved PM2.5 prediction improved daily estimates of PM2.5 as it leveraged the additional hourly information to well capture the transport pattern, with the increased R2 (from 0.72 to 0.77) and decreased RMSE (from 15.54 to 14.30 μg/m3) at ground monitoring site sparse area. The annual averaged PM2.5 estimated by hourly model also exhibit noticeable differences (∼6%) from that estimated with daily model, implying the latter might suffer overestimation (∼3%) in population-weighted PM2.5 exposure across China. In addition, our new PM2.5 dataset shows the Chinese average PM2.5 is highest around 24:00, lowest around 17:00.Synopsis: By performing the wavelet decomposition on meteorological factors/AOD (independent variables), the hourly seamless PM2.5 concentration across China is well estimated with improved spatiotemporal connection.

Structure and dynamics of urban freight truck movements: A complex network theory perspective

Knowledge of the core structure and inherent dynamics of urban freight transport systems is important for the development of policies, aimed at improving the livability and sustainability of cities. The past decade has witnessed a great deal of efforts into analyzing the geographic structure of urban freight transport systems. However, in-depth studies on the system core structure and underlying dynamics are still absent. This study contributes to the field by analyzing large scale freight truck trip data from Chinese cities, using complex network analysis. We empirically reconstruct and characterize the urban freight truck mobility networks and reveal the underlying spatial interaction patterns. We develop a spatial network growth model which explains how hub-and-spoke core structure of urban freight transport systems are formed. The developed model captures the essential interaction dynamics of freight locations, and explains the effects of spatial distance, economic size and business pattern replication. Inspired by the model, we provide policy implications for land-use planning, transportation planning and sustainable urban development.

Reviewing from a Distance: Uncovering Asymmetric Moderations of Spatial and Temporal Distance between Sentiment Negativity and Rating

Drawing on construal level theory, prior literature has found a positivity bias in online ratings when consumers evaluate an experience from a psychological distance, whether spatial or temporal. Self-distancing theory posits that psychological distance enables individuals to reflect on psychologically distant negative experiences more genuinely, in a less biased way. This raises the question of whether the positivity bias in ratings due to psychological distance persists for negative experiences. To address this question, we collected data from a large review platform that enables the identification of reviewers’ spatial and temporal distance. The negativity of an experience was operationalized via review text sentiment. We introduced spatial and temporal distance as moderators between sentiment negativity and ratings and found a negative moderation by spatial distance and a positive moderation by temporal distance. Our findings indicate that the relationship between sentiment negativity and rating grows stronger under spatial distance and gets weaker under temporal distance. Text mining confirmed self-distancing as the driver behind the spatial moderation and construal levels as the driver behind the temporal moderation. We attribute the asymmetric moderations to differences in the tangibility of spatial distance (more tangible) and temporal distance (less tangible). These results improve our understanding of reviewing behavior and can help platforms de-bias ratings.

Thinning occurrence points does not improve species distribution model performance

AbstractSpatial biases are an intrinsic feature of occurrence data used in species distribution models (SDMs). Thinning species occurrences, where records close in the geographic or environmental space are removed from the modeling procedure, is an approach often used to address these biases. However, thinning occurrence data can also negatively affect SDM performance, given that the benefits of removing spatial biases might be outweighed by the detrimental effects of data loss caused by this approach. We used real and virtual species to evaluate how spatial and environmental thinning affected different performance metrics of four SDM methods. The occurrence data of virtual species were sampled randomly, evenly spaced, and clustered in the geographic space to simulate different types of spatial biases, and several spatial and environmental thinning distances were used to thin the occurrence data. Null datasets were also generated for each thinning distance where we randomly removed the same number of occurrences by a thinning distance and compared the results of the thinned and null datasets. We found that spatially or environmentally thinned occurrence data is no better than randomly removing them, given that thinned datasets performed similarly to null datasets. Specifically, spatial and environmental thinning led to a general decrease in model performances across all SDM methods. These results were observed for real and virtual species, were positively associated with thinning distance, and were consistent across the different types of spatial biases. Our results suggest that thinning occurrence data usually fails to improve SDM performance and that the use of thinning approaches when modeling species distributions should be considered carefully.

Epidemiological Assessment of African Swine Fever Spread in the Dominican Republic.

Since African Swine Fever (ASF) was detected in the Dominican Republic in July 2021, it has negatively impacted the country's swine industry. Assessing the epidemiological situation is crucial to helping local authorities and industry stakeholders control the disease. Here, data on 155 reported outbreaks in the Dominican Republic from November 2022 to June 2023 were evaluated. Descriptive spatiotemporal analysis was performed to characterize disease distribution and spread, and between-herd R0 was calculated for the study period. The Knox test and a space-time permutation model were used to evaluate clustering. Data on clinical presentation, biosecurity measures, and suspected reasons for introduction were categorized and summarized. The majority (78%) of outbreaks occurred on backyard farms which generally had low biosecurity. Across farm types, the majority of pigs were still alive at the time of depopulation. Spatiotemporal findings and R0 estimates suggest an endemic pattern of disease geographically located centrally within the country. Clustering was detected even at small temporal and spatial distances due to outbreaks amongst neighboring backyard farms. These results provide critical information on the current state of the ASF epidemic in the Dominican Republic and will aid government officials and swine industry leaders in developing effective ASF control strategies.

Face Authentication using MTCNN and FaceNet

Abstract: Advancements in facial recognition are on the rise after the recent breakthroughs in deep learning technologies and the extensive training datasets. But still, the practical application of facial recognition for authentication purposes faces some difficulties and problems when dealing with variations that are observed in real-world scenarios, including variations in facial poses, varying angles, lightning conditions and potential obstructions. In order to resolve these challenges, this paper presents an innovative deep learning API, which combines the capabilities of MTCNN and FaceNet, to overcome these limitations. Building upon the foundations of MTCNN and FaceNet, we have developed an API capable of verifying the identity of users through a two-step verification process. The system utilizes MTCNN for precise face detection and FaceNet for facial feature extraction that has the ability to directly map facial images into a compact Euclidean space in which spatial distances directly reflect facial similarity, and then storing this data securely within a MongoDB database. We have used the cutting-edge Deep Face model, developed by Facebook’s AI research team. Its reliable deep learning capabilities have helped us in achieving exceptional accuracy in the user’s verification process. This system serves to provides a superior level of user authentication, effectively reducing the risk of an unauthorized access while optimizing the user experience. Even though there is still some room for improvement within our experiment, we are committed to providing a comprehensive summary of our findings and an exploration of the challenges on the horizon.

Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty

In this study, we aim to develop a machine learning model to predict the level of coordination between two players in tacit coordination games by analyzing the similarity of their spatial EEG features. We present an analysis, demonstrating the model’s sensitivity, which was assessed through three conventional measures (precision, recall, and f1 score) based on the EEG patterns. These measures are evaluated in relation to the coordination task difficulty, as determined by the coordination index (CI). Tacit coordination games are games in which two individuals are requested to select the same option out of a closed set without the ability to communicate. This study aims to examine the effect of the difficulty of a semantic coordination task on the ability to predict a successful coordination between two players based on the compatibility between their EEG signals. The difficulty of each of the coordination tasks was estimated based on the degree of dispersion of the different answers given by the players reflected by the CI. The classification of the spatial distance between each pair of individual brain patterns, analyzed using the random walk algorithm, was used to predict whether successful coordination occurred or not. The classification performance was obtained for each game individually, i.e., for each different complexity level, via recall and precision indices. The results showed that the classifier performance depended on the CI, that is, on the level of coordination difficulty. These results, along with possibilities for future research, are discussed.

Assessing the structure and diversity of fungal community in plant soil under different climatic and vegetation conditions

IntroductionUnderstanding microbial communities in diverse ecosystems is crucial for unraveling the intricate relationships among microorganisms, their environment, and ecosystem processes. In this study, we investigated differences in the fungal community structure and diversity in soils from two contrasting climatic and vegetation conditions: the Xinjiang western China plateau and the Fujian southeastern coastal province.MethodsA total of 36 soil samples collected from two climatic regions were subjected to high-throughput ITS gene sequencing for fungal community analysis. In conjunction soil physicochemical properties were assessed and compared. Analyses included an examination of the relationship of fungal community structure to environmental factors and functional profiling of the community structure was using the FUNGuild pipeline.ResultsOur data revealed rich fungal diversity, with a total of 11 fungal phyla, 31 classes, 86 orders, 200 families, 388 genera, and 515 species identified in the soil samples. Distinct variations in the physicochemical properties of the soil and fungal community structure were seen in relation to climate and surface vegetation. Notably, despite a colder climate, the rhizosphere soil of Xinjiang exhibited higher fungal (α-)diversity compared to the rhizosphere soil of Fujian. β-diversity analyses indicated that soil heterogeneity and differences in fungal community structure were primarily influenced by spatial distance limitations and vegetation type. Furthermore, we identified dominant fungal phyla with significant roles in energy cycling and organic matter degradation, including members of the Sordariomycetes, Leotiomycetes, Archaeosporomycetes, and Agaricomycetes. Functional analyses of soil fungal communities highlighted distinct microbial ecological functions in Xinjiang and Fujian soils. Xinjiang soil was characterized by a focus on wood and plant saprotrophy, and endophytes, whereas in Fujian soil the fungal community was mainly associated with ectomycorrhizal interactions, fungal parasitism, and wood saprotrophy.DiscussionOur findings suggest fungal communities in different climatic conditions adapt along distinct patterns with, plants to cope with environmental stress and contribute significantly to energy metabolism and material cycling within soil-plant systems. This study provides valuable insights into the ecological diversity of fungal communities driven by geological and environmental factors.

Root-centric β diversity reveals functional homogeneity while phylogenetic heterogeneity in a subtropical forest.

Root-centric studies have revealed fast taxonomic turnover across root neighborhoods, but how such turnover is accompanied by changes in species functions and phylogeny (i.e., β diversity) remains largely unknown. As β diversity can reflect the degree of community-wide biotic homogenization, such information is crucial for better inference of below-ground assembly rules, community structuring, and ecosystem processes. We collected 2480 root segments from 625 0-30 cm soil profiles in a subtropical forest in China. Root segments were identified into 138 species with DNA-barcoding with six root morphological and architectural traits measured per species. By using the mean pairwise (Dpw ) and mean nearest neighbor distance (Dnn ) to quantify species ecological differences, we first tested the non-random functional and phylogenetic turnover of root neighborhoods that would lend more support to deterministic over stochastic community assembly processes. Additionally, we examined the distance-decay pattern of β diversity, and finally partitioned β diversity into geographical and environmental components to infer their potential drivers of environmental filtering, dispersal limitation, and biotic interactions. We found that functional turnover was often lower than expected given the taxonomic turnover, whereas phylogenetic turnover was often higher than expected. Phylogenetic Dpw (e.g., interfamily species) turnover exhibited a distance-decay pattern, likely reflecting limited dispersal or abiotic filtering that leads to the spatial aggregation of specific plant lineages. Conversely, both functional and phylogenetic Dnn (e.g., intrageneric species) exhibited an inverted distance-decay pattern, likely reflecting strong biotic interactions among spatially and phylogenetically close species leading to phylogenetic and functional divergence. While the spatial distance was generally a better predictor of β diversity than environmental distance, the joint effect of environmental and spatial distance usually overrode their respective pure effects. These findings suggest that root neighborhood functional homogeneity may somewhat increase forest resilience after disturbance by exhibiting an insurance effect. Likewise, root neighborhood phylogenetic heterogeneity may enhance plant fitness by hindering the transmission of host-specific pathogens through root networks or by promoting interspecific niche complementarity not captured by species functions. Our study highlights the potential role of root-centric β diversity in mediating community structures and functions largely ignored in previous studies.

A study on table tennis landing point detection algorithm based on spatial domain information

To address the limitations of computer vision-assisted table tennis ball detection, which heavily relies on vision acquisition equipment and exhibits slow processing speed, we propose a real-time calculation method for determining the landing point of table tennis balls. This novel approach is based on spatial domain information and reduces the dependency on vision acquisition equipment. This method incorporates several steps: employing dynamic color thresholding to determine the centroid coordinates of all objects in the video frames, utilizing target area thresholding and spatial Euclidean distance to eliminate interference balls and noise, optimizing the total number of video frames through keyframe extraction to reduce the number of operations for object recognition and landing point detection, and employing the four-frame difference slope method and polygonal area determination to detect the landing point and area of the target object, thereby obtaining precise coordinates and their corresponding areas. Experimental results on the above method on the Jetson Nano development board show that the dynamic color thresholding method achieves a detection speed of 45.3 fps. The keyframe extraction method correctly identifies the landing point frames with an accuracy rate exceeding 93.3%. In terms of drop point detection, the proposed method achieves 78.5% overall accuracy in detecting table tennis ball drop points while ensuring real-time detection. These experiments validate that the proposed method has the ability to detect table tennis ball drop points in real time and accurately in low frame rate vision acquisition devices and real environments.

Study on departure passenger movement during security check process at airport terminal based on an improved cellular automata model

Airport security check is a crucial part of air travel, and the efficiency of this process directly impacts the overall airport experience. This article presents a study on the modelling and simulation of movement of departure passengers during security check at airport terminals, with a particular emphasis on elucidating microscopic properties such as passenger flow patterns, individual movement dynamics and especially queuing phenomena. In order to solve the unreasonable path problem caused by direct application of cellular automata model, we propose an improved cellular automata model in which a new concept of horizontal restrain floor field is innovatively introduced. Considering the factors of spatial distance, number of queuing people and potential queuing people, a checkpoint selection probability model is embedded into the CA model. The results show that the queuing behavior of passengers is influenced by various factors from three aspects: (i) selection strategy of checkpoint, (ii) spatial layout of inspection area, (iii) arrival interval and verification time. The findings of this study can be used by airport operators to have better understanding of departure passengers’ movements at a micro level, improve the efficiency of the security check process and enhance the airport experience for passengers. Some measures can be taken to reduce the queue waiting time, such as personnel guidance, information signage, planning wider entrance and more queue lanes, replacing the manual verification with ticket inspection machines and so on.

A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation

Human activity intensity prediction, i.e., estimating the dynamic population distribution, is crucial to many location-based applications, particularly intelligent transportation systems and urban planning. Geographical artificial intelligence (GeoAI) is an emerging field that has contributed many new modeling approaches for spatiotemporal phenomena, especially spatiotemporal graph neural networks (ST-GNNs). However, there are two challenges to the prediction of human activity intensity. On the one hand, accurate predictions require modeling not only pairwise spatial relationships, e.g., distance and spatial flow but also complex high-order relationships, such as clusters and groups. On the other hand, it is not appropriate to incorporate prior knowledge on the spatial structure of data with subjective assumptions using graphs with predefined topologies or weights. To mitigate such challenges, a novel deep learning-based model was proposed, namely, the Hypergraph-based Hybrid Graph Convolutional Network (HyGCN). In our study, high-order relationships are modeled by hypergraph convolutions, and pairwise relationships are captured in graph convolutions. A spatial fusion layer is added to fuse the pairwise and high-order relationships. Then, adaptive graph and hypergraph generation methods are proposed to interpret edge weights and understand the learned geographic relationships in accordance with principles. After systematic experiments on large-scale mobility data, our results demonstrate that HyGCN outperforms state-of-the-art models. The learned geospatial knowledge can be disentangled and explained from the perspectives of geographic principles. This study offers new insights for not only revisiting geographic laws in GeoAI but also utilizing spatiotemporal knowledge in deep learning models.

Assessment and simulation of thermal environments in Taiyuan urban built-up area, China

The urban heat island (UHI) effect has intensified with increases in impervious surface areas and population densities due to urbanization, which affects the quality of urban life and ecological services. Here, the Moran’s I and hot spot analysis (Getis-Ord Gi*) are used to explore spatial autocorrelation of land surface temperature (LST) in Taiyuan built-up area. Then, the built-up area is divided to 41 sub-areas to accurately explore the urban LST differences caused by different LULC types. Moreover, geographically weighted regression (GWR) is used to analysis the spatial heterogeneity of LST. Finally, we simulate the LST changes using the CA-Markov model in the study area in the year 2030. Our results showed that 1) average LST was 22.76°C in Taiyuan built-up area in 2018. The Highest-temperature areas were distributed in heavy-industry intensive areas in the north, north central, and southeast, whereas the Lowest-temperature areas mainly corresponded to rivers, lakes, urban forests, and green spaces. 2) The Moran’s I gradually decreased from 0.8635 to 0.2097 with an increase in the spatial distance threshold. The optimal recognition effect was obtained at a 400 × 400-m scale. The Getis-Ord Gi* analysis indicated that the cold area was 1248.32 km2 (12.24% of the study area) and the hot area was 43.84 km2 (11.11% of the study area) in 2018. 3) The GWR analysis showed significant spatial non-stationarity in the influence of LULC types on LST. The GWR model was calculated with reference to the observation values of the adjacent areas, so as to better reveal the spatial relationship between artificial surface, woodland, water, grassland, and bare land and LST. 4) The UHI distribution was more concentrated in 2030 than in 2021. The statistics of the proportion and transfer matrix of LST indicated that the proportion of the Highest and Lowest-temperature areas in 2030 decreased and the UHI effect will further intensify. This study could be used to guide sustainable development in cities and provides theoretical support for adjusting the urban spatial structure.

The relation between biodiversity in literature and social and spatial situation of authors: Reflections on the nature–culture entanglement

Abstract Understanding the nature–culture entanglement by combining the methods of natural sciences and humanities is little approached in neither of the fields. With a specific combination of methods from both digital humanities and ecology, we aimed at identifying several of people's life circumstances that relate to their individual sensitivity towards biodiversity. The circumstances with a strong correlation could be considered and targeted by decision‐makers, for example by developing specific education programmes for making people more eco‐conscious or adjusting relevant regulations. We applied machine learning techniques onto a database including information about the frequency of biodiversity mentioned in creative literature (BiL) from 1705 to 1969 as response variable related to metadata about the corresponding works and their authors as predictors, including localisation, age, gender and literature genre. The algorithm determined the response's dependency on each predictor, which can be interpreted as the intensity of this particular sensitivity parameter for biodiversity, and which we also related to time. We recognised that gender, age, region and settlement size are predictors significantly correlated to BiL. Statistically, these predictors can be viewed as starting points of the eventual individual level of awareness for biodiversity. For example, authors from villages exhibit a higher BiL than those from cities, which we interpret as a signal for the dependence of awareness for biodiversity on spatial distance from nature, which in turn can be addressed in urban development. Our conclusion is that applying a machine learning technique on literary data yields meaningful results, thereby showing potential for further similar investigations and the combination of methods from natural sciences and humanities to achieve so far unattainable insights. With our study, these insights could contribute to ecologically based decision‐making processes. Read the free Plain Language Summary for this article on the Journal blog.

Intercropping with Trifolium repens contributes disease suppression of banana Fusarium wilt by reshaping soil protistan communities

Fusarium wilt disease of bananas, caused by the soil-borne pathogen Fusarium oxysporum, threatens banana production. Intercropping, cultivation of more than one crop simultaneously on the same field, has emerged as efficient and sustainable land management for suppressing Fusarium wilt disease. Although previous studies have proven the changes in soil microbial communities including bacteria and fungi under intercropping contributed to disease suppression, little is known about the role of protistan communities in driving this effect. In a field experiment, we assessed microbiome shifts with a focus on protists under intercropping of banana with the legume Trifolium repens. Our results showed that the reduced Fusarium wilt disease incidence and the decreased Fusarium pathogen density under intercropping could be attributed to the changes in protistan community compositions. Specially, we observed a significant negative correlation between the relative abundance of phagotrophic protists and Fusarium oxysporum. We further conducted a pot experiment to examine the impacts of the legume crop on phagotrophic protists across different spatial distances, which showing that intercropping enriched the relative abundance of phagotrophic protists, especially Cercomonas, with members of this group showing the capacity to directly inhibit the growth of Fusarium pathogen in vitro. We highlight that predatory protists are important agents underlying disease suppression in intercropping system, which can offer new venues to promote plant health in sustainable agriculture.

HRRSODN: high-Resolution remote sensing object detection network fusion of road-adjacent relationships

Application of deep learning technology in remote sensing image target detection(RSITD)has made remarkable progress in recent years. However, existing methods usually fuse spatial context information at the pixel level while ignoring the mining of spatial relationships between ground objects. This paper proposes a high-resolution remote sensing object detection network fusion of road-adjacent relationships (HRRSODN) that integrates road proximity relations. Taking artificial features such as toll stations adjacent to roads as an example, the spatial relationship of road proximity is used to assist visual information and improve the extraction accuracy. The method optimizes detection by: Using the spatial relationship as prior information; fusing coordinate attention that captures the spatial relationship between objects; and adding the attention generated by the spatial relationship to the attention generated in the original X and Y directions. This enables the model to understand the spatial layout of objects in the image. The distance loss function concretizes the spatial relationship into a distance index. The distance loss is incorporated into the training process by measuring the spatial distance between feature targets. Thus, during training, the network focuses not only on the positional information of the targets but also pays more attention to the spatial layout of the features. Experimental results show that, compared with conventional methods, the proposed method has achieved significant improvements in detection performance and mining spatial relationships. This verifies the importance of considering the spatial relationship of ground objects in remote sensing target detection. Further, this paper proposes a new method for target detection in remote sensing images (RSIs), which effectively utilizes the spatial relationship information between targets, and provides a new idea for further improving target detection in RSI.