Space Patterns Research Articles

Research on identification and zoning control of territorial spatial risk pattern based on deep learning: A case study of Shenzhen, China

In the era of globalization, digitalization, and diversification, currently societal cities face risks intertwined by uncertainty and complexity. Thus, it is crucial to address risks concerns to achieve modernization of spatial management. Moreover, how to scientifically identify the risk pattern of territorial space and how to map and control it in space becomes a critical and urgent issue to be addressed for achieving modernization of spatial management. Understanding the intricate relationship between land use, environmental factors, and population density is essential for identifying risk patterns that can inform effective disaster management strategies. By employing advanced analytical methods, this study aims to provide a comprehensive framework for recognizing and mitigating territorial spatial risks in an increasingly complex urban landscape. This study used a case study to conduct the unsupervised algorithm of stack autoencoder self-organizing map to analyze the distribution and spatial clustering of Shenzhen's land spatial risk pattern and reveal the driving mechanism of its risk pattern formation in detail. It was found that: (1) The overall territorial spatial risk pattern of Shenzhen presents a distribution trend of "high in the west and low in the east, high in the south and low in the north"; that is, I and II risk areas are clustered in the hilly areas in the northeast and IV and V risk areas in the coastal areas in the southwest; (2) Population density, land use intensity, seawater intrusion, geological disasters, relief amplitude, soil type, soil erosion intensity are the main driving forces for the formation of Shenzhen's territorial spatial risk pattern. In particular, seawater intrusion, population density and land use intensity, soil type were found having significantly synergistic impact on the risk pattern.

Impact of urban landscape patterns on butterfly diversity in Fuzhou City parks

Butterflies are crucial bioindicators of urban environmental changes. Utilizing urban green spaces to create ecological corridors and habitats is essential for maintaining biodiversity. Therefore, understanding the factors that influence landscape patterns in urban green spaces and their impact on butterfly diversity is crucial. This study investigated how landscape patterns in urban green spaces affect butterfly diversity through a year-long survey across 24 urban parks in Fuzhou. The main findings were as follows: (1) A total of 7786 butterflies from 59 species were recorded, with the highest species diversity and richness observed in Fushan Country Park. Seasonal abundance peaked in summer, while richness was highest in autumn. Species composition varied significantly across park types. (2) Monophagous butterfly diversity was positively correlated with woodland proportion and negatively to waterbody proportion. Polyphagous and oligophagous butterfly diversity showed positive correlations with woodland aggregation index and grassland proportion, but negative with construction land proportion. Factors such as the woodland proportion and grassland proportion influenced butterfly diversity in different activity spaces. (3) Butterfly communities with different feeding types and vertical activity space ranges responded differently to landscape pattern indices. Seasonal variations indicated that polyphagous butterflies and those occupying the middle activity space exhibited similar responses to environmental factors.

Study on the Method of Vineyard Information Extraction Based on Spectral and Texture Features of GF-6 Satellite Imagery

Accurately identifying the distribution of vineyard cultivation is of great significance for the development of the grape industry and the optimization of planting structures. Traditional remote sensing techniques for vineyard identification primarily depend on machine learning algorithms based on spectral features. However, the spectral reflectance similarities between grapevines and other orchard vegetation lead to persistent misclassification and omission errors across various machine learning algorithms. As a perennial vine plant, grapes are cultivated using trellis systems, displaying regular row spacing and distinctive strip-like texture patterns in high-resolution satellite imagery. This study selected the main oasis area of Turpan City in Xinjiang, China, as the research area. First, this study extracted both spectral and texture features based on GF-6 satellite imagery, subsequently employing the Boruta algorithm to discern the relative significance of these remote sensing features. Then, this study constructed vineyard information extraction models by integrating spectral and texture features, using machine learning algorithms including Naive Bayes (NB), Support Vector Machines (SVMs), and Random Forests (RFs). The efficacy of various machine learning algorithms and remote sensing features in extracting vineyard information was subsequently evaluated and compared. The results indicate that three spectral features and five texture features under a 7 × 7 window have significant sensitivity to vineyard recognition. These spectral features include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), while texture features include contrast statistics in the near-infrared band (B4_CO) and the variance statistic, contrast statistic, heterogeneity statistic, and correlation statistic derived from NDVI images (NDVI_VA, NDVI_CO, NDVI_DI, and NDVI_COR). The RF algorithm significantly outperforms both the NB and SVM models in extracting vineyard information, boasting an impressive accuracy of 93.89% and a Kappa coefficient of 0.89. This marks a 12.25% increase in accuracy and a 0.11 increment in the Kappa coefficient over the NB model, as well as an 8.02% enhancement in accuracy and a 0.06 rise in the Kappa coefficient compared to the SVM model. Moreover, the RF model, which amalgamates spectral and texture features, exhibits a notable 13.59% increase in accuracy versus the spectral-only model and a 14.92% improvement over the texture-only model. This underscores the efficacy of the RF model in harnessing the spectral and textural attributes of GF-6 imagery for the precise extraction of vineyard data, offering valuable theoretical and methodological insights for future vineyard identification and information retrieval efforts.

Constraining dark matter from strong phase transitions in a U1Lμ−Lτ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{U}{(1)}_{L_{\\mu }-{L}_{\ au }} $$\\end{document} model: implications for neutrino masses and muon g − 2

In this paper, we study a non-minimal gauged U1Lμ−Lτ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{U}{(1)}_{L_{\\mu }-{L}_{\ au }} $$\\end{document} model, where we add two complex singlet scalars, three right-handed Majorana neutrinos (RHN), and a vector-like dark fermion to the Standard Model (SM), all non-trivially charged under the extra gauge symmetry. The model offers an easy resolution to the muon (g – 2) anomaly, which fixes the scale of spontaneous symmetry breaking. Furthermore, the two-zero minor structure in the RHN mass matrix provides successful predictions for neutrino oscillation parameters, including the Dirac phase. The extended scalar sector can easily induce first-order phase transitions. We identify all possible phase transition patterns in the three-dimensional field space. We quantify the associated gravitational waves from the sound wave source and demonstrate that the signatures can be observed in future space-based experiments. We find that strong first-order phase transitions require large values of scalar quartic couplings which constrain the scalar dark matter (DM) relic density to a maximum of 10−2 and 10−5 when we consider the DM direct detection bound. Nonetheless, the model successfully explains the DM relic density via contribution from the vector-like dark fermion. We show the allowed range of the model parameters that can address all the beyond SM issues targeted in this study.

Integrating Graph Structures into Kernel Regression Models

Kernel methods are highly flexible and powerful tools for capturing complex, nonlinear relationships in data. In this paper, we propose a substantial extension of existing network-based regression models by integrating kernel methods with graph-theoretic constraints. Our approach builds upon the foundational work of Li et al. [1], who incorporated network cohesion into generalized linear models (GLMs). We extend their framework by introducing a kernelized regression model that allows for the modeling of nonlinear interactions while leveraging network data. This kernelized framework relaxes the linearity constraints of GLMs, making the model more versatile and capable of capturing complex patterns in high-dimensional spaces. We demonstrate the effectiveness of our approach using simulated and real-world datasets, such as the Teenage Friends and Lifestyle Study. Results show that our kernelized network regression model not only outperforms traditional linear and generalized linear models in predictive accuracy but also scales efficiently to larger datasets. Our work represents a significant advancement in the modeling of network-linked data, providing a robust, scalable, and interpretable framework that extends the application of kernel methods beyond their traditional constraints. Future directions include exploring more complex graph structures, such as weighted and directed graphs, and developing optimized algorithms for even larger datasets.

Existence and uniqueness of the solution to initial and inverse problems for integro-differential heat equations with fractional load

This work is devoted to the unique solvability of the direct and inverse problems for a multidimensional heat equation with a fractional load in Holder spaces. In the problem under consideration, the loaded term is in the form of a fractional integral operator for the time variable. We prove the existence and uniqueness of the solution to these problems by the contraction mapping theorem and the theory of integral equations. For more information see https://ejde.math.txstate.edu/Volumes/2024/64/abstr.html

Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example

Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation.

Reconstructing the Ocean State Using Argo Data and a Data-Driven Method

Abstract Over the past two decades, Argo profiles have provided unprecedented insight into the global patterns of space and time variability of ocean temperature and salinity, significantly reducing associated uncertainties. However, analyzing such assessments during the pre-Argo period remains challenging due to the scarcity of observations in many regions. From the Argo period, a set of dominant three-dimensional patterns can be estimated using Empirical Orthogonal Function (EOF) analysis, which helps to fill in observational gaps. From the associated principal components, temporal fluctuations can be observed, aiming to build a catalog of possible ocean state trajectories. To map pre-Argo observations, EOFs are used in a data assimilation framework that uses this catalog to feed an analog prediction and provide reanalysis. In this study, a new data-driven interpolation method called RedAnDA (Reduced-space Analog Data Assimilation) was tested in the tropical Pacific Ocean. RedAnDA was first validated through an Observing System Simulation Experiment (OSSE) approach, using synthetic observations extracted from a model simulation. It was subsequently applied to a real historical dataset and compared with other available reanalysis products. Overall, the reconstructed temperature field showed variability consistent with the OSSE true field and other reanalysis products in the real data application. Further improvements are needed to optimally estimate uncertainty, but RedAnDA already combines valuable information about state predictability, observational sampling, and unresolved scale issues.

Impact of green space patterns on PM2.5 levels: A local climate zone perspective

Green spaces have been demonstrated to significantly decrease PM2.5 levels. However, the impact of green spaces on PM2.5 levels in different spatial forms of urban blocks is not yet fully understood. This research utilized ensemble machine learning algorithms to investigate the impact of green space spatial patterns, assessed through landscape pattern analysis, to PM2.5 concentrations during summer and winter based on local climate zones (LCZ). The results revealed significant differences in PM2.5 concentration across the seven types of primary LCZs, primarily influenced by meteorological factors. In winter, eight green space indicators exhibited a more substantial contribution to PM2.5 levels in comparison to the summer season. Significant discrepancies were noted in the contributions of these indicators across different LCZs. Patch density (PD) and landscape shape index (LSI) made a more substantial contribution, while relative patch richness (RPR) and Shannon's evenness index (SHEI) showed a less significant contribution. The spatial pattern of green spaces was significantly related to their contributions. Among the seven predominant LCZs, the five key indicators included PD, LSI, Shannon's diversity index (SHDI), area-weighted mean patch area (AREA_AM), and connectance index (CONNECT). Spatial heterogeneity was further observed in the positive and negative contributions of each green space indicator. This study enhances understanding of the impact of green spaces on PM2.5 across LCZs and provides valuable insights for urban management and planning.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

A reservoir computing approach in active noise control for vehicle applications

Reservoir Computing (RC) is an alternative approach to Recurrent Neural Networks (RNN) that can avoid some shortcomings of conventional gradient decent-training that are mainly related to high computational complexity and the reduced ability to "learn" long-range dependencies. In RC the input data are transformed into patterns in a high-dimensional space by an RNN called the "reservoir", which remains unchanged during training. The desired output signal is generated as a linear combination of the neuron's signals from the input-excited reservoir. The linear part of the RC architecture, which is called the "readout" can be trained with a simple learning algorithm such as linear regression. In this work an RC architecture is proposed for the attenuation of acoustic disturbances encountered in vehicle cabins such as aircrafts and yachts. At first, a dataset of acoustic pressure measurements from real-world cabins was used to generate the readout and reservoir layers. Subsequent computer simulations demonstrated that the proposed architecture can successfully attenuate such acoustic disturbances. Finally, the performance of RC in Active Noise Cancellation task was compared with both the linear FxLMS algorithm and a conventional RNN, showing that it has several advantages regarding acoustic pressure reduction combined with relatively low computational complexity.

Spatial Fantasies in Contemporary Russian Cinema

Fantastic film worlds give the viewer a new experience of understanding and living in space – combining different locations, establishing and overcoming boundaries, transitions into a fascinating other reality. This article examines the spatial patterns that create arenas of action for characters in Russian films and series of fantasy-mystical content shot in the 2020s. Analyses of worldbuilding in science fiction are related to philosophical ideas about possible worlds and are most represented by discussions of fantasy literature, whereas film fiction and the spaces it depicts await research attention. The authors dissect spatial patterns in sci-fi cinema as a reflection of current social conflicts/divisions and solidarities/convergences; reflecting on how spatial configurations encode the unprecedented mediation of modern technologies and normalize futuristic spatial practices. The focus is on the recurring film-to-film emphasis on the permeability, hybridity and tunnelling of space, on the phantasmagoria of non-releasing places. Russian cinema responds to an ontological philosophical agenda by visualizing the permeability of boundaries and the co-presence of man and other beings. The analysis shows that the portals and roads linking dream and reality, Soviet and modern Moscow, the cyber village on Mars and other planets of the solar system bear a significant plot load. The movement of the characters in an extraordinary space reproduces the archetypal test of the road and provides an opportunity to discuss social and attitudinal issues relevant to the Russian present. Particular attention is paid to the pattern of non-releasing space, which concentrates the fear of isolation characteristic of the established culture of mobility. Science fiction in cinema immerses viewers in worlds inhabited by unusual creatures, filled with artefacts, infrastructure, often through plot tension, frightening hints, visual effects draws attention to significant problems and challenges, including in the discussion of images of the future.

Exploring the Potential Relationship between Cooling Green Space and Built-up area: Analysis of Community Green Space Characteristics Based on GWPCA

Rapid urbanization exacerbates urban heat island (UHI) problem. Although urban green space (UGS) can produce multiple benefits for mitigating UHI, the accompanying problems of urban development cause multiple pressures on greenery. Therefore, the pattern of green spaces should be adjusted to adapt to the current situation of the built-up area, fully utilizing its cooling effect, which is of great significance for achieving sustainable and balanced urban development. We selected 849 community units in Harbin city for the study, calculated the urban green space pattern index (UGSP) based on geographically weighted principal component analysis (GWPCA), And used bivariate choropleth map and Cartesian coordinate system to explore the spatial relationship between UGSP and associated characteristics of urbanization (e.g., land function diversity and development intensity). The results showed the following. (1) Compared with traditional PCA, GWPCA can capture local details of UGSP, especially proportion of tree area was the most sensitive factor in the urban core area, and FRAC_MN and LSI were the sensitive factors in the west and south of urban outer areas, respectively. (2) The matching degree between development intensity (I) and UGSP was slightly better than that between land function diversity (D) and UGSP, and showed a more egalitarian phenomenon. (3) The equilibrium value (dLow) presented a relatively random spatial distribution when UGSP was less than D/I, and the unbalanced value (dHigh) was scattered in the core area. Both dLow and dHigh were concentrated outside the urban core area when UGSP was greater than D/I. Our study provides an effective guidance for community greenery to mitigate UHI and realize the harmonious coexistence between city and nature.

Multi-temporal drought rarity curves—A yearly classification of meteorological drought severity in France

Droughts are recurrent phenomena that present a large variety of space and time patterns making rather difficult the assessment of their rarity and the comparison between events.Our study focuses on the “memory effect” of meteorological drought over France using the daily raingauge network maintained by Météo-France over 1950–2022. The proposed easy tool of rarity curves analyzes how drought events build and persist across time. The approach is purely statistic, assuming that drought consequences depend on the probability of non exceedance (“rarity”) of antecedent rainfall accumulations. In order to cover a large spectrum of “memory effects”, we consider a continuum of accumulation periods ranging from a few weeks to several years. The rarity curve of a given year displays the most severe rarity values encountered during the year as a function of the various accumulation periods (256 values ranging from 4 to 260 weeks in our case).Over the study period of 1950–2022 we show how the shape of rarity curves discriminate short- and long-term historical droughts. A k-means algorithm proves to be useful to classify the different years into typical drought situations from continuous drying to continuous wetting over the antecedent five years. Looking at the chronology of the found clusters also allows studying how droughts build and persist across time.

Nanoscale Electron Beam Patterning of PEDOT:PSS Free‐Standing Films for Enhanced Thermoelectric Performance

The growing demand for flexible, lightweight, and highly processable electronic devices makes high‐functionality conducting polymers such as poly (3,4‐ethylene dioxythiophene): polystyrene sulfonate (PEDOT:PSS) an attractive alternative to conventional inorganic materials for various applications including thermoelectrics. However, considerable improvements are necessary to make conducting polymers a commercially viable choice for thermoelectric applications. This study explores nanopatterning as an effective and unique strategy for enhancing polymer functionality to optimize thermoelectric parameters, such as electrical conductivity, Seebeck coefficient, and thermal conductivity. Introducing nanopatterning into thermoelectric polymers is challenging due to intricate technical hurdles and the necessity for individually manipulating the interdependent thermoelectric parameters. Here, array nanopatterns with different pattern spacings are imposed on free‐standing PEDOT:PSS films using direct electron beam irradiation, thereby achieving selective control of electrical and thermal transport in PEDOT:PSS. Electron beam irradiation transformed PEDOT:PSS from a highly ordered quinoid to an amorphous benzoid structure. Optimized pattern spacing resulted in a remarkable 70% reduction in thermal conductivity and a 60% increase in thermoelectric figure of merit compared to non‐patterned PEDOT:PSS. The proposed nanopatterning methodology demonstrates a skillful approach to precisely manipulate the thermoelectric parameters, thereby improving the thermoelectric performance of conducting polymers, and promising utilization in cutting‐edge electronic applications.

Does Urban Green Space Pattern Affect Green Space Noise Reduction?

The effect of urban green spaces on traffic noise reduction has been extensively studied at the level of single vegetation, hedges, etc., but there is a lack of corresponding studies at the scale of spatial patterns of urban green spaces. Therefore, this study aims to analyze the relationship between the spatial pattern of urban green space and the change in green space’s noise reduction capacity. Through the morphology spatial pattern analysis method, this analysis divides the urban green space in the Fuzhou high-tech zone into seven types of elements with different ecological definitions and simulates the noise condition of the urban environment with the presence of green space as well as without the presence of green space by computer simulation, calculates the distribution map of the noise reduction produced by the urban green space, and analyzes the correlation between the seven types of green space elements and the noise reduction with the geographically weighted regression modeling analysis. The study finds that (1) Urban green space patterns can significantly affect the net noise reduction of green space. Areas with high green coverage can produce a stronger green space noise reduction effect. (2) More complex green space shapes and more fragmented urban green space can produce higher noise reduction. (3) The green space close to the source of noise can exert a stronger noise reduction effect. Therefore, in the process of planning and design, from the perspective of improving the urban acoustic environment, the configuration of high-quality green spaces in areas with higher levels of noise pollution should be given priority, which may have better noise reduction effects.

Smartphone Usage Behavior And Technology Acceptance In Oil Palm Replanting

Replanting activities need support using information technology so that plantation business actors can access information related to plantations independently and accurately. This research analyzes smartphone usage behavior using a technology acceptance model approach among oil palm users. This research was conducted at the Karya Dharma III Village Unit Cooperative, Keranji Guguh Village, Koto Gasib District, Siak Regency. The sample taken in this research was 30 planters. The sampling technique in this research uses a snowball sampling technique, which is obtained through a rolling process based on the relationship between one respondent and another. Snowball sampling is a sampling method that uses initial illustration data to identify other illustrations that meet the criteria. Data analysis was carried out using a descriptive study based on a Likert scale. The final results of this research show that using smartphones among oil palm planters in Keranji Guguh Village as a medium to help search for and receive information for oil palm planters is classified as moderate, with an average score of 2.84. Smartphone use has become an alternative media planters use to search for information on supporting oil palm rejuvenation activities, land clearing, planting spacing patterns, using superior seeds, and plant maintenance replanting.

Influence of Tree Geometry on Growth and Yield of Mungbean (Vigna radiata L.) under Gamhar (Gmelina arborea Roxb.) based Agroforestry System

The experiment was conducted during kharif (June to September) 2022–23 at Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India to evaluate effect of Gmelina arborea geometry on the growth and yield of mungbean, tree growth, biomass, carbon storage and assessing the economic feasibility of the agroforestry systems. The experiment was designed for intercropping of one mungbean high yielding varieties viz., IPM 205-7 (Virat) sown in June with Gmelina arborea. The treatments for gamhar and mungbean are as follows: T1-Vigna radiata sole crop; T2-Gmelina arborea sole tree (5×5 m2); T3-Gmelina arborea sole tree (5×4 m2); T4-Gmelina arborea sole tree (5×3 m2); T5-Gmelina arborea (5×5 m2)+Vigna radiata; T6-Gmelina arborea (5×4 m2)+Vigna radiata; T7-Gmelina arborea (5×3 m2)+Vigna radiata. Experiment was designed in randomized block design (RBD) with seven treatments and five replications. The effect of different spacing patterns viz., S1-5×5 m2, S2-5×4 m2, S3-5×3 m2 in 2 year old Gamhar plantation with seven treatments combinations on mungbean was studied at the Forestry Research Farm, RLBCAU, Jhansi. The maximum number of branches plant-1 (8.04), number of trifoliate leaves plant-1 (11.96), number of seeds pod-1 (12.32), grain yield (0.73 t ha-1) and straw yield (2.13 t ha-1) was recorded in mungbean sole crop. The plant height (62.51 cm) was recorded highest in mungbean intercrop in 5×3 m2 gamhar spacing. The maximum girth (27.38 cm) of trees was recorded in 5×5 m2 gamhar spacing mungbean intercrop. The tree height (6.01 m) and benefit cost ratio (2.67) was observed maximum in 5×4 m2 gamhar spacing with mungbean intercrop.

Research on the Identification, Network Construction, and Optimization of Ecological Spaces in Metropolitan Areas Based on the Concept of Production-Living-Ecological Space

In the strategic orientation of promoting high-quality development of metropolitan areas, ecological space is not only the core carrier for maintaining regional ecological balance and harmony but also a key element in shaping the scientific layout of metropolitan areas and promoting harmonious coexistence between cities and nature. This paper takes the Chang-Zhu-Tan metropolitan area as the research object and identifies and classifies ecological space based on the “Production-Life-Ecology” concept, extracts core ecological source areas through the minimum resistance model, and generates ecological resistance surfaces. Then, three types of ecological space corridors are constructed by using the MCR gravity model. This study finds that the ecological space in the Chang-Zhu-Tan metropolitan area is seriously fragmented, the number of corridors is insufficient, and the spatial configuration of the “Production-Life-Ecology” is imbalanced in the region and proposes optimization models and strategies in combination with the spatial network approach to identify and classify ecological space patterns for the metropolitan area. Accordingly, the study proposes optimization models and strategies based on the spatial network approach to provide theoretical support and practical guidance for the ecological spatial pattern and sustainable development of the metropolitan area.

Cross‐validation matters in species distribution models: a case study with goatfish species

In an era of ongoing biodiversity, it is critical to map biodiversity patterns in space and time for better‐informing conservation and management. Species distribution models (SDMs) are widely applied in various types of such biodiversity assessments. Cross‐validation represents a prevalent approach to assess the discrimination capacity of a target SDM algorithm and determine its optimal parameters. Several alternative cross‐validation methods exist; however, the influence of choosing a specific cross‐validation method on SDM performance and predictions remains unresolved. Here, we tested the performance of random versus spatial cross‐validation methods for SDM using goatfishes (Actinopteri: Syngnathiformes: Mullidae) as a case study, which are recognized as indicator species for coastal waters. Our results showed that the random versus spatial cross‐validation methods resulted in different optimal model parameterizations in 57 out of 60 modeled species. Significant difference existed in predictive performance between the random and spatial cross‐validation methods, and the two cross‐validation methods yielded different projected present‐day spatial distribution and future projection patterns of goatfishes under climate change exposure. Despite the disparity in species distributions, both approaches consistently suggested the Indo‐Australian Archipelago as the hotspot of goatfish species richness and also as the most vulnerable area to climate change. Our findings highlight that the choice of cross‐validation method is an overlooked source of uncertainty in SDM studies. Meanwhile, the consistency in richness predictions highlights the usefulness of SDMs in marine conservation. These findings emphasize that we should pay special attention to the selection of cross‐validation methods in SDM studies.